|
|
|
|
|
|
IETF RFC 9675
Last modified on Thursday, November 7th, 2024
 Permanent link to RFC 9675
 Search GitHub Wiki for RFC 9675
 Show other RFCs mentioning RFC 9675
Internet Engineering Task Force (IETF) E. Birrane, III
Request for Comments: 9675 S. Heiner
Category: Informational E. Annis
ISSN: 2070-1721 JHU/APL
November 2024
Delay-Tolerant Networking Management Architecture (DTNMA)
 Abstract
The Delay-Tolerant Networking (DTN) architecture describes a type of
challenged network in which communications may be significantly
affected by long signal propagation delays, frequent link
disruptions, or both. The unique characteristics of this environment
require a unique approach to network management that supports
asynchronous transport, autonomous local control, and a small
footprint (in both resources and dependencies) so as to deploy on
constrained devices.
This document describes a DTN Management Architecture (DTNMA)
suitable for managing devices in any challenged environment but, in
particular, those communicating using the DTN Bundle Protocol (BP).
Operating over BP requires an architecture that neither presumes
synchronized transport behavior nor relies on query-response
mechanisms. Implementations compliant with this DTNMA should expect
to successfully operate in extremely challenging conditions, such as
over unidirectional links and other places where BP is the preferred
transport.
Status of This Memo
This document is not an Internet Standards Track specification; it is
published for informational purposes.
This document is a product of the Internet Engineering Task Force
(IETF). It represents the consensus of the IETF community. It has
received public review and has been approved for publication by the
Internet Engineering Steering Group (IESG). Not all documents
approved by the IESG are candidates for any level of Internet
Standard; see Section 2 of RFC 7841.
Information about the current status of this document, any errata,
and how to provide feedback on it may be obtained at
https://www.rfc-editor.org/info/RFC 9675.
Copyright Notice
Copyright (c) 2024 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
(https://trustee.ietf.org/license-info) in effect on the date of
publication of this document. Please review these documents
carefully, as they describe your rights and restrictions with respect
to this document. Code Components extracted from this document must
include Revised BSD License text as described in Section 4.e of the
Trust Legal Provisions and are provided without warranty as described
in the Revised BSD License.
Table of Contents
1. Introduction
1.1. Purpose
1.2. Scope
1.3. Organization
2. Terminology
3. Challenged Network Overview
3.1. Challenged Network Constraints
3.2. Topology and Service Implications
3.2.1. Tiered Management
3.2.2. Remote and Local Manager Associations
3.3. Management Special Cases
4. Desirable Design Properties
4.1. Dynamic Architectures
4.2. Hierarchically Modeled Information
4.3. Adaptive Push of Information
4.4. Efficient Data Encoding
4.5. Universal, Unique Data Identification
4.6. Runtime Data Definitions
4.7. Autonomous Operation
5. Current Remote Management Approaches
5.1. SNMP and SMI Models
5.1.1. The SMI Modeling Language
5.1.2. SNMP and Transport
5.2. XML-Infoset-Based Protocols and YANG Data Models
5.2.1. The YANG Modeling Language
5.2.2. NETCONF Protocol and Transport
5.2.3. RESTCONF Protocol and Transport
5.2.4. CORECONF Protocol and Transport
5.3. gRPC Network Management Interface (gNMI)
5.3.1. The Protobuf Modeling Language
5.3.2. gRPC Protocol and Transport
5.4. Intelligent Platform Management Interface (IPMI)
5.5. Autonomic Networking
5.6. Deep Space Autonomy
6. Motivation for New Features
7. Reference Model
7.1. Important Concepts
7.2. Model Overview
7.3. Functional Elements
7.3.1. Managed Applications and Services
7.3.2. DTNMA Agent (DA)
7.3.3. Managing Applications and Services
7.3.4. DTNMA Manager (DM)
7.3.5. Pre-Shared Definitions
8. Desired Services
8.1. Local Monitoring and Control
8.2. Local Data Fusion
8.3. Remote Configuration
8.4. Remote Reporting
8.5. Authorization
9. Logical Autonomy Model
9.1. Overview
9.2. Model Characteristics
9.3. Data Value Representation
9.4. Data Reporting
9.5. Command Execution
9.6. Predicate Autonomy Rules
10. Use Cases
10.1. Notation
10.2. Serialized Management
10.3. Intermittent Connectivity
10.4. Open-Loop Reporting
10.5. Multiple Administrative Domains
10.6. Cascading Management
11. IANA Considerations
12. Security Considerations
13. Informative References
Acknowledgements
Authors' Addresses
1. Introduction
This document describes a logical, informational Delay-Tolerant
Networking Management Architecture (DTNMA) suitable for operating
devices in a challenged architecture, such as those communicating
using the DTN Bundle Protocol version 7 (BPv7) [RFC 9171].
Challenged networks have certain properties that differentiate them
from other kinds of networks. These properties, outlined in
Section 2.2.1 of [RFC 7228], include lacking end-to-end IP
connectivity, having "serious interruptions" to end-to-end
connectivity, and exhibiting delays longer than can be tolerated by
end-to-end synchronization mechanisms (such as TCP).
These challenged network properties can be caused by a variety of
factors such as physical constraints (e.g., long signal propagation
delays and frequent link disruptions), administrative policies (e.g.,
quality-of-service prioritization, service-level agreements, and
traffic management and scheduling), and off-nominal behaviors (e.g.,
active attackers and misconfigurations). Since these challenges are
not solely caused by sparseness, instances of challenged networks
will persist even in increasingly connected environments.
The DTN architecture, described in [RFC 4838], has been designed for
data exchange in challenged networks. Just as the DTN architecture
requires new capabilities for transport and transport security,
special consideration is needed for the operation of devices in a
challenged network.
1.1. Purpose
This document describes how challenged network properties affect the
operation of devices in such networks. This description is presented
as a logical architecture formed from a union of best practices for
operating devices deployed in challenged environments.
One important practice captured in this document is the concept of
self-operation. Self-operation involves operating a device without
human interactivity, without system-in-the-loop synchronous
functions, and without a synchronous underlying transport layer.
This means that devices determine their own schedules for data
reporting, determine their own operational configuration, and perform
their own error discovery and mitigation.
1.2. Scope
This document includes the information necessary to document existing
practices for operating devices in a challenged network in the
context of a logical architecture. A logical architecture describes
the logical operation of a system by identifying components of the
system (such as in a reference model), the behaviors they enable, and
use cases describing how those behaviors result in the desired
operation of the system.
Logical architectures are not functional architectures. Therefore,
any functional design for achieving desired behaviors is out of scope
for this document. The set of architectural principles presented
here is not meant to completely specify interfaces between
components.
The selection of any particular transport or network layer is outside
of the scope of this document. The DTNMA does not require the use of
any specific protocol such as IP, BP, TCP, or UDP. In particular,
the DTNMA design does not presume the use of BPv7, IPv4, or IPv6.
| NOTE: As BPv7 is the preferred transport for networks
| conforming to the DTN architecture, the DTNMA should be
| considered for any BPv7 network deployment. However, the DTNMA
| may also be used in other networks that have similar needs for
| this particular style of self-operation. For this reason, the
| DTNMA does not require the use of BPv7 to transport management
| information.
Network features such as naming, addressing, routing, and
communications security are out of scope for the DTNMA. It is
presumed that any operational network communicating DTNMA messages
would implement these services for any payloads carried by that
network.
The interactions between and amongst the DTNMA and other management
approaches are outside of the scope of this document.
1.3. Organization
The following nine sections provide details regarding the DTNMA.
Terminology: Section 2 identifies terms fundamental to understanding
DTNMA concepts. Whenever possible, these terms align in both word
selection and meaning with their use in other management
protocols.
Challenged Network Overview: Section 3 describes important aspects
of challenged networks and necessary approaches for their
management.
Desirable Design Properties: Section 4 defines those properties of
the DTNMA needed to operate within the constraints of a challenged
network. These properties are similar to the specification of
system-level requirements of a DTN management solution.
Current Remote Management Approaches: Section 5 provides a brief
overview of existing remote management approaches. Where
possible, the DTNMA adopts concepts from these approaches.
Motivation for New Features: Section 6 provides an overall
motivation for this work. It also explains why a management
architecture for challenged networks is useful and necessary.
Reference Model: Section 7 defines a reference model that can be
used to analyze the DTNMA independently of an implementation or
implementation architecture. This model identifies the logical
components of the system and the high-level relationships and
behaviors amongst those components.
Desired Services: Section 8 identifies and defines the DTNMA
services provided to network and mission operators.
Logical Autonomy Model: Section 9 provides an example data model
that can be used to analyze DTNMA control and data flows. This
model is based on the DTNMA reference model.
Use Cases: Section 10 presents multiple use cases accommodated by
the DTNMA. Each use case is presented as a set of control and
data flows referencing the DTNMA reference model and logical
autonomy model.
2. Terminology
This section defines terminology that is either unique to the DTNMA
or necessary for understanding the concepts defined in this
specification.
Timely Data Exchange: The ability to communicate information between
two (or more) entities within a required period of time. In some
cases, a 1-second exchange may not be timely; in other cases, a
1-hour exchange may be timely.
Local Operation: The operation of a device by an application co-
resident on that device. Local operators are applications running
on a device, and there might be one or more of these applications
working independently or as one to perform the local operations
function. Absent error conditions, local operators are always
expected to be available to the devices they manage.
Remote Operation: The operation of a device by an application
running on a separate device. Remote operators communicate with
operated devices over a network. Remote operators are not always
expected to be available to the devices they operate.
DTN Management: The management, monitoring, and control of a device
that does not depend on stateful connections, timely data exchange
of management messages, or system-in-the-loop synchronous
functions. DTN management is accomplished as a fusion of local
operation and remote operation techniques; remote operators manage
the configuration of local operators who provide monitoring and
control of their co-resident devices.
DTNMA Agent (DA): A role associated with a managed device
responsible for reporting performance data, accepting policy
directives, performing autonomous local control, error handling,
and data validation. DAs exchange information with DTNMA Managers
(DMs) operating on the same device and/or on remote devices in the
network. A DA is a type of local operator.
DTNMA Manager (DM): A role associated with a managing device
responsible for configuring the behavior of, and eventually
receiving information from, DAs. DMs interact with one or more
DAs located on the same device and/or on remote devices in the
network. A DM is a type of remote operator.
Controls: Procedures run by a DA to change the behavior,
configuration, or state of an application or protocol managed by
that DA. These include procedures to manage the DA itself, such
as having the DA produce performance reports or applying new
management policies.
Externally Defined Data (EDD): Typed information made available to a
DA by its hosting device but not computed directly by the DA
itself.
Data Report: A typed, ordered collection of data values gathered by
one or more DAs and provided to one or more DMs. Reports comply
with the format of a given data report schema.
Data Report Schema: A named, ordered collection of data elements
that represent the schema of a data report.
Rule: Unit of autonomous specification that provides a stimulus-
response relationship between time or state on a DA and the
actions or operations to be run as a result of that time or state.
3. Challenged Network Overview
The DTNMA provides network management services able to operate in
challenged network environments for which the DTN architecture was
created. This section describes what is meant by the term
"challenged network", the important properties of such a network, and
observations on impacts to management approaches.
3.1. Challenged Network Constraints
Constrained networks are defined as networks where "some of the
characteristics pretty much taken for granted with link layers in
common use in the Internet at the time of writing are not attainable"
[RFC 7228]. This broad definition captures a variety of potential
issues relating to physical, technical, and regulatory constraints on
message transmission. Constrained networks typically include nodes
that regularly reboot or are otherwise turned off for long periods of
time, transmit at low or asynchronous bitrates, and/or have very
limited computational resources.
Separately, a challenged network is defined as one that "has serious
trouble maintaining what an application would today expect of the
end-to-end IP model" [RFC 7228]. Links in such networks may be
impacted by attenuation, propagation delays, mobility, occultation,
and other limitations imposed by energy and mass considerations.
Therefore, systems relying on such links cannot guarantee timely end-
to-end data exchange.
| NOTE: Because challenged networks might not provide services
| expected of the end-to-end IP model, devices in such networks
| might not implement networking stacks associated with the end-
| to-end IP model. This means that devices might not include
| support for certain transport protocols (TCP/QUIC/UDP), web
| protocols (HTTP), or internetworking protocols (IPv4/IPv6).
By these definitions, a "challenged" network is a special type of
"constrained" network, where constraints prevent timely end-to-end
data exchange. As such, "All challenged networks are constrained
networks ... but not all constrained networks are challenged networks
... Delay-Tolerant Networking (DTN) has been designed to cope with
challenged networks" [RFC 7228].
Solutions that work in constrained networks might not be solutions
that work in challenged networks. In particular, challenged networks
exhibit the following properties that impact the way in which the
function of network management is considered.
* Timely end-to-end data exchange cannot be guaranteed to exist at
any given time between any two nodes.
* Latencies on the order of seconds, hours, or days must be
tolerated.
* Managed devices cannot be guaranteed to always be powered so as to
receive ad hoc management requests (even requests with
artificially extended timeout values).
* Individual links may be unidirectional.
* Bidirectional links may have asymmetric data rates.
* The existence of external infrastructure, services, systems, or
processes such as a Domain Name System (DNS) or a Certificate
Authority (CA) cannot be guaranteed.
3.2. Topology and Service Implications
The set of constraints that might be present in a challenged network
impacts both the topology of the network and the services active
within that network.
Operational networks handle cases where nodes join and leave the
network over time. These topology changes may or may not be planned,
they may or may not represent errors, and they may or may not impact
network services. Challenged networks differ from other networks not
in the presence of topological change but in the likelihood that
impacts to topology result in impacts to network services.
The difference between topology impacts and service impacts can be
expressed in terms of connectivity. Topological connectivity usually
refers to the existence of a path between an application message
source and destination. Service connectivity, alternatively, refers
to the existence of a path between a node and one or more services
needed to process -- often just in time -- application messaging.
Examples of service connectivity include access to infrastructure
services such as a Domain Name System (DNS) or a CA.
In networks that might be partitioned most of the time, it is less
likely that a node would concurrently access both an application
endpoint and one or more network service endpoints. For this reason,
network services in a challenged network should be designed to allow
for asynchronous operation. Accommodating this use case often
involves the use of local caching, pre-placing information, and not
hard-coding message information at a source that might change when a
message reaches its destination.
| NOTE: One example of rethinking services in a challenged
| network is the securing of BPv7 bundles. The Bundle Protocol
| Security (BPSec) [RFC 9172] security extensions to BPv7 do not
| encode security destinations when applying security. Instead,
| BPSec requires nodes in a network to identify themselves as
| security verifiers or acceptors when receiving and processing
| secured messages.
3.2.1. Tiered Management
Network operations and management approaches need to adapt to the
topology and service impacts encountered in challenged networks. In
particular, the roles and responsibilities of "managers" and "agents"
need to be different than what would be expected of unchallenged
networks.
When connectivity to a manager cannot be guaranteed, agents will need
to rely on locally available information and local autonomy to react
to changes at the node. When an agent uses local autonomy for self-
operation, it acts as a local operator serving as a proxy for an
absent remote operator.
Therefore, the role of a "manager" must become one of a remote
operator generating configurations and other essential updates for
the local operator "agents" that are co-resident on a managed device.
This approach creates a two-tiered management architecture. The
first tier is the management of the local operator configuration
using any one of a variety of standard mechanisms, models, and
protocols. The second tier is the operation of the local device
through the local operator.
The DTNMA defines the DTNMA Manager (DM) as a remote operator
application and the DTNMA Agent (DA) as an agent acting as a local
operator application. In this model, which is illustrated in
Figure 1, the DM and DA can be viewed as applications, with the DM
producing new configurations and the DA receiving those
configurations from an underlying management mechanism.
_
/
/ +------------+ +-----------+ Local +---------+
TIER / | DM (Remote | | DA (Local | Operation | Managed |
2 \ | Operator) | | Operator) | <---------> | Apps |
MGMT \ +------------+ +-----------+ +---------+
\_ ^ ^
| configs | configs
_ | |
/ V V
/ +------------+ Remote +------------+
TIER / | Management | Management | Management |
1 \ | Client | <----------> | Server |
MGMT \ +------------+ +------------+
\_
Figure 1: Two-Tiered Management Architecture
In this approach, the configurations produced by the DM are based on
the DA features and associated data model. How those configurations
are transported between the DM and the DA, and how results are
communicated back from the DA to the DM, can be accomplished using
whatever mechanism is most appropriate for the network and the device
platforms -- for example, the use of a Network Configuration Protocol
(NETCONF), RESTCONF, or Simple Network Management Protocol (SNMP)
server on the managed device to provide configurations to a DA.
3.2.2. Remote and Local Manager Associations
In addition to disconnectivity, topological change can alter the
associations amongst managed and managing devices. Different
managing devices might be active in a network at different times or
in different partitions. Managed devices might communicate with
some, all, or none of these managing devices as a function of their
own local configuration and policy.
| NOTE: These concepts relate to practices in conventional
| networks. For example, supporting multiple managing devices is
| similar to deploying multiple instances of a network service
| such as a DNS server or CA node. Selecting from a set of
| managing devices is similar to a sensor node's practice of
| electing cluster heads to act as privileged nodes for data
| storage and exfiltration.
Therefore, a network management architecture for challenged networks
should:
1. Support a many-to-many association amongst managing and managed
devices, and
2. Allow "control from" and "reporting to" managing devices to
function independently of one another.
3.3. Management Special Cases
The following special cases illustrate some of the operational
situations that can be encountered in the management of devices in a
challenged network.
One-Way Management: A managed device can only be accessed via a
unidirectional link or via a link whose duration is shorter than a
single round-trip propagation time. Results of this management
may come back at a different time, over a different path, and/or
as observable from out-of-band changes to device behavior.
Summary Data: A managing device might only receive summary data
regarding a managed device's state because a link or path is
constrained by capacity or reliability.
Bulk Historical Reporting: A managing device receives a large volume
of historical report data for a managed device. This can occur
when a managed device rejoins a network or has temporary access to
a high-capacity link (or path) between itself and the managing
device.
Multiple Managers: A managed device tracks multiple managers in the
network and communicates with them as a function of time, local
state, or network topology. This scenario would also apply to
challenged networks that interconnect two or more unchallenged
networks such that managed and managing devices exist in different
networks.
These special cases highlight the need for managed devices to operate
without presupposing a dedicated connection to a single managing
device. Managing devices in a challenged network might never expect
a reply to a command, and communications from managed devices may be
delivered much later than the events being reported.
4. Desirable Design Properties
This section describes those design properties that are desirable
when defining a management architecture operating across challenged
links in a network. These properties ensure that network management
capabilities are retained even as delays and disruptions in the
network scale. Ultimately, these properties are the driving design
principles for the DTNMA.
| NOTE: These properties may influence the design, construction,
| and adaptation of existing management tools for use in
| challenged networks. For example, the properties of the DTN
| architecture [RFC 4838] resulted in the development of BPv7
| [RFC 9171] and BPSec [RFC 9172]. Implementing the DTNMA model
| may result in the construction of new management data models,
| policy expressions, and/or protocols.
4.1. Dynamic Architectures
The DTNMA should be agnostic to the underlying physical topology,
transport protocols, security solutions, and supporting
infrastructure of a given network. Due to the likelihood of
operating in a frequently partitioned environment, the topology of a
network may change over time. Attempts to stabilize an architecture
around individual nodes can result in a brittle management framework
and the creation of congestion points during periods of connectivity.
The DTNMA should not prescribe any association between a DM and a DA
other than those defined in this document. There should be no
logical limitation on the number of DMs that can control a DA, the
number of DMs that a DA should report to, or any requirement that a
DM and DA relationship imply a pair.
| NOTE: Practical limitations on the relationships between and
| amongst DMs and DAs will exist as a function of the
| capabilities of networked devices. These limitations derive
| from processing and storage constraints, performance
| requirements, and other engineering factors. Implementors of
| managed and managing devices must account for these limitations
| in their device designs.
4.2. Hierarchically Modeled Information
The DTNMA should use data models to define the syntactic and semantic
contracts for data exchange between a DA and a DM. A given model
should have the ability to "inherit" the contents of other models to
form hierarchical data relationships.
| NOTE: The term "data model" in this context refers to a schema
| that defines a contract between a DA and a DM regarding how
| information is represented and validated.
Many network management solutions use data models to specify the
semantic and syntactic representation of data exchanged between
managed and managing devices. The DTNMA is not different in this
regard; information exchanged between DAs and DMs should conform to
one or more predefined, normative data models.
A common best practice when defining a data model is to make it
cohesive. A cohesive model is one that includes information related
to a single purpose such as managing a single application or
protocol. When applying this practice, it is not uncommon to develop
a large number of small data models that, together, describe the
information needed to manage a device.
Another best practice for data model development is the use of
inclusion mechanisms to allow one data model to include information
from another data model. This ability to include a data model avoids
repeating information in different data models. When one data model
includes information from another data model, there is an implied
model hierarchy.
Data models in the DTNMA should allow for the construction of both
cohesive models and hierarchically related models. These data models
should be used to define all sources of information that can be
retrieved, configured, or executed in the DTNMA. These actions would
include supporting DA autonomy functions such as parameterization,
filtering, and event-driven behaviors. These models will be used to
both implement interoperable autonomy engines on DAs and define
interoperable report parsing mechanisms on DMs.
| NOTE: While data model hierarchies can result in a more concise
| data model, arbitrarily complex nesting schemes can also result
| in very verbose encodings. Where possible, data identification
| schemes should be constructed that allow for both hierarchical
| data and highly compressible data identification.
4.3. Adaptive Push of Information
DAs in the DTNMA should determine when to push information to DMs as
a function of their local state.
"Pull" management mechanisms require a managing device to send a
query to a managed device and then wait for a response to that
specific query. This practice implies some knowledge synchronization
between entities (which may be as simple as knowing when a managed
device might be powered). However, challenged networks cannot
guarantee timely round-trip data exchange. For this reason, pull
mechanisms should be avoided in the DTNMA.
"Push" mechanisms, in this context, indicate the ability of DAs to
leverage local autonomy to determine when and what information should
be sent to which DMs. The push is considered adaptive because a DA
determines what information to push (and when) as an adaptation to
changes to the DA's internal state. Once pushed, information might
still be queued, pending connectivity of the DA to the network.
Even in cases where a round-trip exchange can occur, pull mechanisms
increase the overall amount of traffic in the network and preclude
the use of autonomy at managed devices. So, even when pull
mechanisms are feasible, they should not be considered a pragmatic
alternative to push mechanisms.
4.4. Efficient Data Encoding
Messages exchanged between a DA and a DM in the DTNMA should be
defined in a way that allows for efficient on-the-wire encoding.
DTNMA design decisions that result in smaller message sizes should be
preferred over those that result in larger message sizes.
There is a relationship between message encoding and message
processing time at a node. Messages with few or no encodings may
simplify node processing, whereas more compact encodings may require
additional activities to generate/parse encoded messages. Generally,
compressing a message takes processing time at the sender and
decompressing a message takes processing time at a receiver.
Therefore, there is a design trade-off between minimizing message
sizes and minimizing node processing.
There is a significant advantage to smaller DTNMA message sizes in a
challenged network. Smaller messages require shorter periods of
viable transmission for communication, they incur less retransmission
cost, and they consume fewer resources when persistently stored en
route in the network.
| NOTE: Naive approaches to minimizing message size through
| general-purpose compression algorithms do not produce minimal
| encodings. Data models can, and should, be designed for
| compact encoding from the beginning. Design strategies for
| compact encodings involve using structured data, hierarchical
| data models, and common substructures within data models.
| These strategies allow for compressibility beyond what would
| otherwise be achieved by computing large hash values over
| generalized data structures.
4.5. Universal, Unique Data Identification
Data elements within the DTNMA should be uniquely identifiable so
that they can be individually manipulated. Further, these
identifiers should be universal -- the identifier for a data element
should be the same, regardless of role, implementation, or network
instance.
Identification schemes that would be relative to a specific DA or
specific system configuration might change over time and should be
avoided. Relying on relative identification schemes would require
resynchronizing relative state when nodes in a challenged network
reconnect after periods of partition. This type of resynchronization
should be avoided whenever possible.
| NOTE: Consider a common management technique for approximating
| an associative array lookup. If a managed device tracks the
| number of bytes passed by multiple named interfaces, then the
| number of bytes through a specific named interface ("int_foo")
| would be retrieved in the following way:
|
| 1. Query a list of ordered interface names from an agent.
|
| 2. Find the name that matches "int_foo", and infer the
| agent's index of "int_foo" from the ordered interface
| list. In this instance, assume that "int_foo" is the
| fourth interface in the list.
|
| 3. Query the agent (again) to now return the number of
| bytes passed through the fourth interface.
|
| Ignoring the inefficiency of two round-trip exchanges, this
| mechanism will fail if an agent implementation changes its
| index mapping between the first and second queries.
|
| The desired data being queried, "number of bytes through
| 'int_foo'", should be uniquely and universally identifiable and
| independent of how that data exists in any agent's custom
| implementation.
4.6. Runtime Data Definitions
The DTNMA allows for the addition of new data elements to a data
model as part of the runtime operation of the management system.
These definitions may represent custom data definitions that are
applicable only for a particular device or network. Custom
definitions should also be able to be removed from the system during
runtime.
The goal of this approach is to dynamically add or remove data
elements to the local runtime schemas as needed, such as the
definition of new counters, new reports, or new rules.
The custom definition of new data from existing data (such as through
data fusion, averaging, sampling, or other mechanisms) provides the
ability to communicate desired information in as compact a form as
possible.
| NOTE: A DM could, for example, define a custom data report that
| includes only summary information about a specific operational
| event or as part of specific debugging. DAs could then produce
| this smaller report until it is no longer necessary, at which
| point the custom report could be removed from the management
| system.
Custom data elements should be calculated and used both as parameters
for DA autonomy and for more efficient reporting to DMs. Defining
new data elements allows for DAs to perform local data fusion, and
defining new reporting templates allows for DMs to specify desired
formats and generally save on link capacity, storage, and processing
time.
4.7. Autonomous Operation
The management of applications by a DA should be achievable using
only knowledge local to the DA because DAs might need to operate
during times when they are disconnected from a DM.
DA autonomy may be used for simple automation of predefined tasks or
to support semi-autonomous behavior in determining when to run tasks
and how to configure or parameterize tasks when they are run.
Important features provided by the DA are listed below. These
features work together to accomplish tasks. As such, there is
commonality amongst their definitions and nature of their benefits.
Standalone Operation: Preconfiguration allows DAs to operate without
regular contact with other nodes in the network. Updates for
configurations remain difficult in a challenged network, but this
approach removes the requirement that a DM be in the loop during
regular operations. Preconfiguring stimuli and responses on a DA
during periods of connectivity allows DAs to self-manage during
periods of disconnectivity.
Deterministic Behavior: Operational systems might need to act in a
deterministic way, even in the absence of an operator in the loop.
Deterministic behavior allows an out-of-contact DM to predict the
state of a DA and to determine how a DA got into a particular
state.
Engine-Based Behavior: Operational systems might not be able to
deploy "mobile code" solutions [RFC 4949] due to network bandwidth,
memory or processor loading, or security concerns. Engine-based
approaches provide configurable behavior without incurring these
concerns.
Authorization and Accounting: The DTNMA does not require a specific
underlying transport protocol, a specific network infrastructure,
or specific network services. Therefore, mechanisms for
authorization and accounting need to be present in a standard way
at DAs and DMs to provide these functions if the underlying
network does not. This is particularly true in cases where
multiple DMs may be active concurrently in the network.
To understand the contributions of these features to a common type of
behavior, consider the example of a managed device coming online with
a set of preinstalled configurations. In this case, the device's
standalone operation comes from the preconfiguration of its local
autonomy engine. This engine-based behavior allows the system to
behave in a deterministic way, and any new configurations will need
to be authorized before being adopted.
Features such as deterministic processing and engine-based behavior
are separate from (but do not preclude the use of) other Artificial
Intelligence (AI) and Machine Learning (ML) approaches for device
management.
5. Current Remote Management Approaches
Several remote management solutions have been developed for both
local area networks and wide area networks. Their capabilities range
from simple configuration and report generation to complex modeling
of device settings, state, and behavior. All of these approaches are
successful in the domains for which they have been built but are not
all equally functional when deployed in a challenged network.
This section describes some of the well-known protocols for remote
management and contrasts their purposes with the desirable properties
of the DTNMA. The purpose of this comparison is to identify parts of
existing approaches that can be adopted or adapted for use in
challenged networks and where new capabilities should be created
specifically for such environments.
5.1. SNMP and SMI Models
An early and widely used example of a remote management protocol is
SNMP, which is currently at version 3 [RFC 3410]. SNMP utilizes a
request-response model to get and set data values within an
arbitrarily deep object hierarchy. Objects are used to identify data
such as host identifiers, link utilization metrics, error rates, and
counters between application software on managing and managed devices
[RFC 3411]. Additionally, SNMP supports a model for unidirectional
push messages, called event notifications, based on agent-defined
triggering events.
SNMP relies on logical sessions with predictable round-trip latency
to support its pull mechanism, but a single activity is likely to
require many round-trip exchanges. Complex management can be
achieved, but only through careful orchestration of real-time, end-
to-end, managing-device-generated query-and-response logic.
There is existing work that uses the SNMP data model to support some
low-fidelity agent-side processing; this work includes using
"Distributed Management Expression MIB" [RFC 2982] and "Definitions of
Managed Objects for the Delegation of Management Scripts" [RFC 3165].
However, agent autonomy is not an SNMP mechanism, so support for a
local agent response to an initiating event is limited. In a
challenged network where the delay between a managing device
receiving an alert and sending a response can be significant, SNMP is
insufficient for autonomous event handling.
5.1.1. The SMI Modeling Language
SNMP separates the representations for managed data models from
messaging, sequencing, and encoding between managers and agents.
Each data model is termed a "Management Information Base" (or "MIB")
[RFC 3418] and uses the Structure of Management Information (SMI)
modeling language [RFC 2578]. Additionally, the SMI itself is based
on the ASN.1 syntax [ASN.1], which is used not just for SMI but for
other, unrelated data structure specifications such as the
Cryptographic Message Syntax (CMS) [RFC 5652]. Separating data models
from messaging and encoding is a best practice in remote management
protocols and is also necessary for the DTNMA.
Each SNMP MIB is composed of managed object definitions, each of
which is associated with a hierarchical Object Identifier (OID).
Because of the arbitrarily deep nature of MIB object trees, the size
of OIDs is not strictly bounded by the protocol (though it may be
bounded by implementations).
5.1.2. SNMP and Transport
SNMPv2 [RFC 3416] [RFC 3417] and SNMPv3 [RFC 3414] can operate over a
variety of transports, including plaintext UDP/IP [RFC 3417], SSH/TCP/
IP [RFC 5592], and DTLS/UDP/IP or TLS/TCP/IP [RFC 6353].
SNMP uses an abstracted security model to provide authentication,
integrity, and confidentiality. There are options for the User-based
Security Model (USM) [RFC 3414], which uses in-message security, and
the Transport Security Model (TSM) [RFC 5591], which relies on the
transport to provide security functions and interfaces.
5.2. XML-Infoset-Based Protocols and YANG Data Models
Several network management protocols, including NETCONF [RFC 6241],
RESTCONF [RFC 8040], and the Constrained Application Protocol (CoAP)
Management Interface (CORECONF) [CORE-COMI], share the same XML
Information Set [xml-infoset] for the information set's hierarchical
managed information and XPath expressions [XPath] to identify nodes
of that information model. Since they share the same information
model and the same data manipulation operations, together they will
be referred to as "*CONF" protocols. Each protocol, however,
provides a different encoding of that information set and its related
operation-specific data.
The YANG modeling language as defined in [RFC 7950] is used to define
the data model for these management protocols. Currently, YANG
represents the IETF standard for defining managed information models.
5.2.1. The YANG Modeling Language
The YANG modeling language defines a syntax and modular semantics for
organizing and accessing a device's configuration or operational
information. YANG allows subdividing a full managed configuration
into separate namespaces defined by separate YANG modules. Once a
module is developed, it is used (directly or indirectly) on both the
client and server to serve as a contract between the two. A YANG
module can be complex, describing a deeply nested and interrelated
set of data nodes, actions, and notifications.
Unlike the separation between ASN.1 syntax and module semantics from
higher-level SMI data model semantics as discussed in Section 5.1.1,
YANG defines both a text syntax and module semantics together with
data model semantics.
The YANG modeling language provides flexibility in the organization
of model objects to the model developer. YANG supports a broad range
of data types as noted in [RFC 6991]. YANG also supports the
definition of parameterized Remote Procedure Calls (RPCs) and actions
to be executed on managed devices as well as the definition of event
notifications within the model.
Current *CONF notification logic allows a client to subscribe to the
delivery of specific containers or data nodes defined in the model,
on either a periodic or "on-change" basis [RFC 8641]. These
notification events can be filtered according to XPath or subtree
filtering [XPath] [RFC 6241] as described in Section 2.2 of [RFC 8639].
The use of YANG for data modeling necessarily comes with some side
effects, some of which are described here.
Text Naming: Data nodes, RPCs, and notifications within a YANG data
model are named by a namespace-qualified, text-based path of the
module, submodule, container, and any data nodes such as lists,
leaf-lists, or leaves, without any explicit hierarchical
organization based on data or object type.
Existing efforts to make compressed names for YANG objects, such
as the YANG Schema Item iDentifiers (SIDs) as discussed in
Section 3.2 of [RFC 9254], allow a node to be named by a globally
unique integer value but are still relatively verbose (up to 8
bytes per item) and still must be translated into text form for
things like instance identification (see below). Additionally,
when representing a tree of named instances, the child elements
can use differential encoding of SID integer values as "delta"
integers. The mechanisms for assigning SIDs and the lifecycle of
those SIDs are discussed in [RFC 9595].
Text Values and Built-In Types: Because the original use of YANG
with NETCONF was to model XML Information Sets, the values and
built-in types are necessarily text based. JSON encoding of YANG
data [RFC 7951] allows for optimized representations of many built-
in types; similarly, Concise Binary Object Representation (CBOR)
encoding [RFC 9254] allows for different optimized representations.
In particular, the YANG built-in types support a fixed range of
decimal fractions (Section 9.3 of [RFC 7950]) but purposefully do
not support floating-point numbers. There are alternatives, such
as the type bandwidth-ieee-float32 [RFC 8294] or using the "binary"
type with one of the IEEE-754 encodings.
Deep Hierarchy: YANG allows for, and current YANG modules take
advantage of, the ability to deeply nest a model hierarchy to
represent complex combinations and compositions of data nodes.
When a model uses a deep hierarchy of nodes, this necessarily
means that the qualified paths to name those nodes and instances
are longer than they would be in a flat namespace.
Instance Identification: The node instances in a YANG module
necessarily use XPath expressions for identification. Some
identification is constrained to be strictly within the YANG
domain, such as "must", "when", "augment", or "deviation"
statements. Other identification needs to be processed by a
managed device -- for example, via the "instance-identifier"
built-in type. This means that any implementation of a managed
device must include XPath processing and related information model
handling per Section 6.4 of [RFC 7950] and its referenced
documents.
Protocol Coupling: A significant amount of existing YANG tooling or
modeling presumes the use of YANG data within a management
protocol with specific operations available. For example, the
access control model defined in [RFC 8341] relies on those
operations specific to the *CONF protocols for proper behavior.
The emergence of multiple NETCONF-derived protocols may make these
presumptions less problematic in the future. Work to more
consistently identify different types of YANG modules and their
use has been undertaken to disambiguate how YANG modules should be
treated [RFC 8199].
Manager-Side Control: YANG RPCs and actions execute on a managed
device and generate an expected, structured response. RPC
execution is strictly limited to those issued by the manager.
Commands are executed immediately and sequentially as they are
received by the managed device, and there is no method to
autonomously execute RPCs triggered by specific events or
conditions.
The YANG modeling language continues to evolve as new features are
needed by adopting management protocols.
5.2.2. NETCONF Protocol and Transport
NETCONF is a stateful, XML-encoding-based protocol that provides a
syntax to retrieve, edit, copy, or delete any data nodes or exposed
functionality on a server. It requires that underlying transport
protocols support long-lived, reliable, low-latency, sequenced data
delivery sessions. A bidirectional NETCONF session needs to be
established before any data transfer (or notification) can occur.
The XML exchanged within NETCONF messages is structured according to
YANG modules supported by the NETCONF agent, and the data nodes
reside within one of possibly many datastores in accordance with the
Network Management Datastore Architecture (NMDA) [RFC 8342].
NETCONF transports are required to provide authentication, data
integrity, confidentiality, and replay protection. Currently,
NETCONF can operate over SSH/TCP/IP [RFC 6242] or TLS/TCP/IP
[RFC 7589].
5.2.3. RESTCONF Protocol and Transport
RESTCONF is a stateless, JSON-encoding-based protocol that provides
the same operations as NETCONF, using the same YANG modules for
structure and the same NMDA datastores, but using RESTful exchanges
over HTTP. It uses HTTP methods to express its allowed operations:
GET, POST, PUT, PATCH, or DELETE data nodes within a datastore.
Although RESTCONF is a logically stateless protocol, it does rely on
state within its transport protocol to achieve behaviors such as
authentication and security sessions. Because RESTCONF uses the same
data node semantics as NETCONF, a typical activity can involve the
use of several sequential round trips of exchanges to first discover
managed device state and then act upon it.
5.2.4. CORECONF Protocol and Transport
CORECONF is an emerging stateless protocol built atop CoAP [RFC 7252]
that defines a messaging construct developed to operate specifically
on constrained devices and networks by limiting message size and
fragmentation. CoAP also implements a request-response system and
methods for GET, POST, PUT, and DELETE.
5.3. gRPC Network Management Interface (gNMI)
Another emerging, but not IETF-affiliated, management protocol is the
gRPC Network Management Interface (gNMI) [gNMI], which is based on
gRPC messaging and uses Google protobuf data modeling.
The same limitations as those listed above for RESTCONF apply to gNMI
because of its reliance on synchronous HTTP exchanges and TLS for
normal operations, as well as the likely deep nesting of data
schemas. gNMI is capable of transporting JSON-encoded YANG-modeled
data, but how to compose such data is not yet fully standardized.
5.3.1. The Protobuf Modeling Language
The data managed and exchanged via gNMI is encoded and modeled using
Google protobuf, an encoding and modeling syntax not affiliated with
the IETF (although an attempt has been made and abandoned
[PROTOCOL-BUFFERS]).
Because the protobuf modeling syntax is a relatively low-level syntax
(about the same as ASN.1 or CBOR), there are some efforts as part of
the OpenConfig work [gNMI] to translate YANG modules into protobuf
schemas (similar to translation to XML or JSON schemas for NETCONF
and RESTCONF, respectively), but there is no required
interoperability between management via gRPC or any of the *CONF
protocols.
5.3.2. gRPC Protocol and Transport
The message encoding and exchange for gNMI, as the name implies, is
the gRPC protocol [gRPC]. gRPC exclusively uses HTTP/2 [RFC 9113] for
transport and relies on some aspects specific to HTTP/2 for its
operations (such as HTTP trailer fields). While not mandated by
gRPC, when used to transport gNMI data, TLS is required for transport
security.
5.4. Intelligent Platform Management Interface (IPMI)
A lower-level remote management protocol, intended to be used to
manage hardware devices and network appliances below the operating
system (OS), is the Intelligent Platform Management Interface (IPMI),
standardized in [IPMI]. The IPMI is focused on health monitoring,
event logging, firmware management, and Serial over LAN (SOL) remote
console access in a "pre-OS or OS-absent" host environment. The IPMI
operates over a companion Remote Management Control Protocol (RMCP)
for messaging, which itself can use UDP for transport.
Because the IPMI and RCMP are tailored to low-level and well-
connected devices within a data center, with typical workflows
requiring many messaging round trips or low-latency interactive
sessions, they are not suitable for operation over a challenged
network.
5.5. Autonomic Networking
The future of network operations requires more autonomous behavior,
including self-configuration, self-management, self-healing, and
self-optimization. One approach to support this is termed "Autonomic
Networking" [RFC 7575].
There is a large and growing set of work within the IETF focused on
developing an Autonomic Networking Integrated Model and Approach
(ANIMA). The ANIMA work has developed a comprehensive reference
model for distributing autonomic functions across multiple nodes in
an Autonomic Networking infrastructure [RFC 8993].
This work, focused on learning the behavior of distributed systems to
predict future events, is an emerging network management capability.
This includes the development of signaling protocols such as the
GeneRic Autonomic Signaling Protocol (GRASP) [RFC 8990] and the
Autonomic Control Plane (ACP) [RFC 8368].
Both autonomic and challenged networks require similar degrees of
autonomy. However, challenged networks cannot provide the complex
coordination between nodes and distributed supporting infrastructure
necessary for the frequent data exchanges for negotiation, learning,
and bootstrapping associated with the above capabilities.
There is some emerging work in ANIMA as to how disconnected devices
might join and leave the ACP over time. However, this work is
addressing a different problem than that encountered by challenged
networks.
5.6. Deep Space Autonomy
Outside of the terrestrial networking community, there are existing
and established remote management systems used for deep space mission
operations. Two examples of such systems are the New Horizons
mission to Pluto [NEW-HORIZONS] and the Double Asteroid Redirection
Test (DART) mission to the asteroid Dimorphos [DART].
The DTNMA has some heritage in the concepts of deep space autonomy,
but each of those mission instantiations uses mission-specific data
encoding, messaging, and transport as well as mission-specific (or
heavily mission-tailored) modeling concepts and languages. Part of
the goal of the DTNMA is to take the proven concepts from these
missions and standardize a messaging syntax as well as a modular data
modeling method.
6. Motivation for New Features
Management mechanisms that provide the complete set of DTNMA
desirable properties do not currently exist. This is not surprising,
since autonomous management in the context of a challenged networking
environment is a new and emerging use case.
In particular, a management architecture is needed that integrates
the following motivating features.
Open-Loop Control: Freedom from a request-response architecture,
API, or other presumption of timely round-trip communications.
This is particularly important when managing networks that are not
built over an HTTP or TCP/TLS infrastructure.
Standard Autonomy Model: An autonomy model that allows for standard
expressions of policy to guarantee deterministic behavior across
devices and vendor implementations.
Compressible Model Structure: A data model that allows for very
compact encodings by defining and exploiting common structures for
data schemas.
Combining these new features with existing mechanisms for message
data exchange (such as BP), data representations (such as CBOR), and
data modeling languages (such as YANG) will form a pragmatic approach
to defining challenged network management.
7. Reference Model
This section describes a reference model for analyzing network
management concepts for challenged networks (generally) and those
conforming to the DTN architecture (in particular). The goal of this
section is to describe how DTNMA services provide DTNMA desirable
properties.
7.1. Important Concepts
Like other network management architectures, the DTNMA draws a
logical distinction between a managed device and a managing device.
Managed devices use a DA to manage resident applications. Managing
devices use a DM to both monitor and control DAs.
The terms "managing" and "managed" represent logical characteristics
of a device and are not, themselves, mutually exclusive. For
example, a managed device might, itself, also manage some other
device in the network. Therefore, a device may support either or
both of these characteristics.
The DTNMA differs from some other management architectures in three
significant ways, all related to the need for a device to self-manage
when disconnected from a managing device.
Pre-Shared Definitions: Managing and managed devices should operate
using pre-shared data definitions and models. This implies that
static definitions should be standardized whenever possible and
that managing and managed devices may need to negotiate
definitions during periods of connectivity.
Agent Self-Management: A managed device may find itself disconnected
from its managing device. In many challenged networking
scenarios, a managed device may spend the majority of its time
without a regular connection to a managing device. In these
cases, DAs manage themselves by applying pre-shared policies
received from managing devices.
Command-Based Interface: Managing devices communicate with managed
devices through a command-based interface. Instead of exchanging
variables, objects, or documents, a managing device issues
commands to be run by a managed device. These commands may create
or update variables, change datastores, or impact the managed
device in ways similar to other network management approaches.
The use of commands is, in part, driven by the need for DAs to
receive updates from both remote management devices and local
autonomy. The use of Controls for the implementation of commands
is discussed in more detail in Section 9.5.
7.2. Model Overview
A DTNMA reference model is provided in Figure 2 below. In this
reference model, applications and services on a managing device
communicate with a DM that uses pre-shared definitions to create a
set of policy directives that can be sent to a managed device's DA
via a command-based interface. The DA provides local monitoring and
control (commanding) of the applications and services resident on the
managed device. The DA also performs local data fusion as necessary
to synthesize data products (such as reports) that can be sent back
to the DM when appropriate.
Managed Device Managing Device
+----------------------------+ +-----------------------------+
| +------------------------+ | | +-------------------------+ |
| |Applications & Services | | | | Applications & Services | |
| +----------^-------------+ | | +-----------^-------------+ |
| | | | | |
| +----------v-------------+ | | +-----------v-------------+ |
| | DTNMA +-------------+ | | | | +-----------+ DTNMA | |
| | AGENT | Monitor and | | |Commanding | | | Policy | MANAGER | |
| | | Control | | |<==========| | | Encoding | | |
| | +------+-------------+ | | | | +-----------+-------+ | |
| | |Admin | Data Fusion | | |==========>| | | Reporting | Admin | | |
| | +------+-------------+ | | Reporting | | +-----------+-------+ | |
| +------------------------+ | | +-------------------------+ |
+----------------------------+ +-----------------------------+
^ ^
| Pre-Shared Definitions |
| +---------------------------+ |
+--------| - Autonomy Model |--------+
| - Application Data Models |
| - Runtime Datastores |
+---------------------------+
Figure 2: DTNMA Reference Model
This model preserves the familiar concept of "managers" resident on
managing devices and "agents" resident on managed devices. However,
the DTNMA model is unique in how the DM and DA operate. The DM is
used to preconfigure DAs in the network with management policies. It
is expected that the DAs, themselves, perform monitoring and control
functions on their own. In this way, a properly configured DA may
operate without a reliable connection back to a DM.
7.3. Functional Elements
The reference model illustrated in Figure 2 implies the existence of
certain logical components whose roles and responsibilities are
discussed in this section.
7.3.1. Managed Applications and Services
By definition, managed applications and services reside on a managed
device. These software entities can be controlled through some
interface by the DA, and their state can be sampled as part of
periodic monitoring. It is presumed that the DA on the managed
device has the proper data model, control interface, and permissions
to alter the configuration and behavior of these software
applications.
7.3.2. DTNMA Agent (DA)
A DA resides on a managed device. As is the case with other network
management approaches, this agent is responsible for the monitoring
and control of the applications local to that device. Unlike other
network management approaches, the agent accomplishes this task
without a regular connection to a DM.
The DA performs three major functions on a managed device: the
monitoring and control of local applications, production of data
analytics, and the administrative control of the agent itself.
7.3.2.1. Monitoring and Control
DAs monitor the status of applications running on their managed
device and selectively control those applications as a function of
that monitoring. The following components are used to perform
monitoring and control on an agent.
Rule Database:
Each DA maintains a database of policy expressions that form rules
regarding the behavior of the managed device. Within this
database, each rule regarding behavior is a tuple of a stimulus
and a response. Within the DTNMA, these rules are the embodiment
of policy expressions received from DMs and evaluated at regular
intervals by the autonomy engine. The rule database is the
collection of active rules known to the DA.
Autonomy Engine:
The DA autonomy engine monitors the state of the managed device,
looking for predefined stimuli and, when such stimuli are
encountered, issuing a predefined response. To the extent that
this function is driven by the rule database, this engine acts as
a policy execution engine. This engine may also be directly
configured by managers during periods of connectivity for actions
separate from those in the rule database (such as enabling or
disabling sets of rules). Once configured, the engine may
function without other access to any managing device. This engine
may also reconfigure itself as a function of policy.
Application Control Interfaces:
DAs support control interfaces for all managed applications.
Control interfaces are used to alter the configuration and
behavior of an application. These interfaces may be custom for
each application or as provided through a common framework,
protocol, or OS.
7.3.2.2. Data Fusion
DAs generate new data elements as a function of the current state of
the managed device and its applications. These new data products may
take the form of individual data values or of new collections of data
used for reporting. The logical components responsible for these
behaviors are as follows.
Application Data Interfaces:
DAs support mechanisms by which important state is retrieved from
various applications resident on the managed device. These data
interfaces may be custom for each application or as provided
through a common framework, protocol, or OS.
Data Value Generators:
DAs may support the generation of new data values as a function of
other values collected from the managed device. These data
generators may be configured with descriptions of data values, and
the data values they generate may be included in the overall
monitoring and reporting associated with the managed device.
Report Generators:
DAs may, as appropriate, generate collections of data values and
provide them to whatever local mechanism takes responsibility for
their eventual transmission (or expiration and removal). Reports
can be generated as a matter of policy or in response to the
handling of critical events (such as errors) or other logging
needs. The generation of a report is independent of whether there
exists any connectivity between a DA and a DM.
7.3.2.3. Administration
DAs perform a variety of administrative services in support of their
configuration, such as the following.
Manager Mapping:
The DTNMA allows for a many-to-many relationship amongst DAs and
DMs. A single DM may configure multiple DAs, and a single DA may
be configured by multiple DMs. Multiple managers may exist in a
network for at least the following two reasons. First, different
managers may exist to control different applications on a device.
Second, multiple managers increase the likelihood of an agent
encountering a manager when operating in a sparse or challenged
environment.
While multiple managers are needed for proper operation in a
dynamically partitioned network, conflicting information from
different managers can result. Implementations of the DTNMA
should consider conflict resolution mechanisms. Such mechanisms
might include analyzing managed content, time, agent location, or
other relevant information to select one manager input over other
manager inputs.
Data Verifiers:
DAs might handle large amounts of data produced by various
sources, to include data from local managed applications, remote
managers, and self-calculated values. DAs should ensure, when
possible, that externally generated data values have the proper
syntax and semantic constraints (e.g., data type and ranges) and
any required authorization.
Access Controllers:
DAs support authorized access to the management of individual
applications, to include the administrative management of the
agent itself. This means that a manager may only set policy on
the agent pursuant to verifying that the manager is authorized to
do so.
7.3.3. Managing Applications and Services
Managing applications and services reside on a managing device and
serve as both the source of DA policy statements and the target of DA
reporting. They may operate with or without an operator in the loop.
Unlike management applications in unchallenged networks, these
applications cannot exert closed-loop control over any managed device
application. Instead, they exercise open-loop control by producing
policies that can be configured and enforced on managed devices by
DAs.
| NOTE: Closed-loop control in this context refers to the
| practice of waiting for a response from a managed device prior
| to issuing new commands to that device. These "loops" may be
| closed quickly (in milliseconds) or over much longer periods
| (hours, days, years). The alternative to closed-loop control
| is open-loop control, where the issuance of new commands is not
| dependent on receiving responses to previous commands.
| Additionally, there might not be a one-to-one mapping between
| commands and responses. A DA may, for example, produce a
| single response that represents the end state of applying
| multiple commands.
7.3.4. DTNMA Manager (DM)
A DM resides on a managing device. This manager provides an
interface between various managing applications and services and the
DAs that enforce their policies. In providing this interface, DMs
translate between whatever innate interface exists to various
managing applications and the autonomy models used to encode
management policy.
The DM performs three major functions on a managing device: policy
encoding, reporting, and administration.
7.3.4.1. Policy Encoding
DMs translate policy directives from managing applications and
services into standardized policy expressions that can be recognized
by DAs. The following logical components are used to perform this
policy encoding.
Application Control Interfaces:
DMs support control interfaces for managing applications. These
control interfaces are used to receive desired policy statements
from applications. These interfaces may be custom for each
application or as provided through a common framework, protocol,
or OS.
Policy Encoders:
DAs implement a standardized autonomy model comprising
standardized data elements. This allows the open-loop control
structures provided by managing applications to be represented in
a common language. Policy encoders perform this encoding
function.
Policy Aggregators:
DMs collect multiple encoded policies into messages that can be
sent to DAs over the network. This implies the proper addressing
of agents and the creation of messages that support store-and-
forward operations. It is recommended that control messages be
packaged using BP bundles when there may be intermittent
connectivity between DMs and DAs.
7.3.4.2. Reporting
DMs receive reports on the status of managed devices during periods
of connectivity with the DAs on those devices. The following logical
components are needed to implement reporting capabilities on a DM.
Report Collectors:
DMs receive reports from DAs in an asynchronous manner. This
means that reports may be received out of chronological order and
in ways that are difficult or impossible to associate with a
specific policy from a managing application. DMs collect these
reports and extract their data in support of subsequent data
analytics.
Data Analyzers:
DMs review sets of data reports from DAs with the purpose of
extracting relevant data to communicate with managing
applications. This may include simple data extraction or may
include more complex processing such as data conversion, data
fusion, and appropriate data analytics.
Application Data Interfaces:
DMs support mechanisms by which data retrieved from DAs may be
provided back to managing devices. These interfaces may be custom
for each application or as provided through a common framework,
protocol, or OS.
7.3.4.3. Administration
DMs in the DTNMA perform a variety of administrative services, such
as the following.
Agent Mappings:
The DTNMA allows DMs to communicate with multiple DAs. However,
not every agent in a network is expected to support the same set
of application data models or otherwise have the same set of
managed applications running. For this reason, DMs determine
individual DA capabilities to ensure that only appropriate
Controls are sent to a DA.
Data Verifiers:
DMs handle large amounts of data produced by various sources, to
include data from managing applications and DAs. DMs should
ensure, when possible, that data values received from DAs over a
network have the proper syntax and semantic constraints (e.g.,
data type and ranges) and any required authorization.
Access Controllers:
DMs should only send Controls to DAs when the manager is
configured with appropriate access to both the agent and the
applications being managed.
7.3.5. Pre-Shared Definitions
A consequence of operating in a challenged environment is the
potential inability to negotiate information in real time. For this
reason, the DTNMA requires that managed and managing devices operate
using pre-shared definitions rather than relying on data definition
negotiation.
The three types of pre-shared definitions in the DTNMA are the DA
autonomy model, managed application data models, and any runtime data
shared by managers and agents.
Autonomy Model:
A DTNMA autonomy model represents the data elements and associated
autonomy structures that define the behavior of the agent autonomy
engine. A standardized autonomy model allows for individual
implementations of DAs and DMs to interoperate. A standardized
model also provides guidance to the design and implementation of
both managed and managing applications.
Application Data Models:
As with other network management architectures, the DTNMA
presupposes that managed applications (and services) define their
own data models. These data models include the data produced by,
and Controls implemented by, the application. These models are
expected to be static for individual applications and standardized
for applications implementing standard protocols.
Runtime Datastores:
Runtime datastores, by definition, include data that is defined at
runtime. As such, the data is not pre-shared prior to the
deployment of DMs and DAs. Pre-sharing in this context means that
DMs and DAs are able to define and synchronize data elements prior
to their operational use in the system. This synchronization
happens during periods of connectivity between DMs and DAs.
8. Desired Services
This section describes the services provided by DTNMA components on
both managing and managed devices. Most of the services discussed in
this section attempt to provide continuous operation of a managed
device through periods of no connectivity with a managing device.
8.1. Local Monitoring and Control
DTNMA monitoring is associated with some DA autonomy engine. The
term "monitoring" implies regular access to information such that
state changes may be acted upon within some response time period.
Predicate autonomy on a managed device should collect state
associated with the device at regular intervals and evaluate that
collected state for any changes that require a preventative or
corrective action. Similarly, this monitoring may cause the device
to generate one or more reports destined to a managing device.
Like monitoring, DTNMA control results in actions by the agent to
change the state or behavior of the managed device. All control in
the DTNMA is local control. In cases where there exists a timely
connection to a DM, received Controls are still evaluated and run
locally as part of local autonomy. In this case, the autonomy
stimulus is the receipt of the Control, and the response is to
immediately run the Control. In this way, there is never a
dependency on a session or other stateful exchange with any remote
entity.
8.2. Local Data Fusion
DTNMA fusion services produce new data products from existing state
on the managed device. These fusion products can be anything from
simple summations of sampled counters to complex calculations of
behavior over time.
Fusion is an important service in the DTNMA because fusion products
are part of the overall state of a managed device. Complete
knowledge of this overall state is important for the management of
the device, and the predicates of rules on a DA may refer to fused
data.
In situ data fusion is an important function, as it allows for the
construction of intermediate summary data, the reduction of stored
and transmitted raw data, and possibly fewer predicates in rule
definitions; this type of data fusion insulates the data source from
conclusions drawn from that data.
The DTNMA requires fusion to occur on the managed device itself. If
the network is partitioned such that no connection to a managing
device is available, then fusion needs to happen locally. Similarly,
connections to a managing device might not remain active long enough
for round-trip data exchange or may not have the bandwidth to send
all sampled data.
| NOTE: The DTNMA does not restrict the storage and transmission
| of raw (pre-fused) data. Such raw data can be useful for
| debugging managed devices, understanding complex interactions
| and underlying conditions, and tuning for better performance
| and/or better outcomes.
8.3. Remote Configuration
DTNMA configuration services update the local configuration of a
managed device with the intent of impacting the behavior and
capabilities of that device.
The DTNMA configuration service is unique in that the selection of
managed device configurations occurs as a function of the state of
the device. This implies that management proxies on the device store
multiple configuration functions that can be applied as needed
without consultation from a managing device.
This approach differs from other management concepts of selecting
from multiple datastores. DTNMA configuration functions can target
individual data elements and can calculate new values from local
device state.
When detecting stimuli, the agent autonomy engine supports a
mechanism for evaluating whether application monitoring data or
runtime data values are recent enough to indicate a change of state.
In cases where data has not been updated recently, it may be
considered stale and therefore not used to reliably indicate that
some stimulus has occurred.
8.4. Remote Reporting
DTNMA reporting services collect information known to the managed
device and prepare it for eventual transmission to one or more
managing devices. The contents of these reports, and the frequency
at which they are generated, occur as a function of the state of the
managed device, independent of the managing device.
Once generated, it is expected that reports might be queued, pending
a connection back to a managing device. Therefore, reports need to
be differentiable as a function of the time they were generated.
| NOTE: When reports are queued pending transmission, the overall
| storage capacity at the queuing device needs to be considered.
| There may be cases where queued reports can be considered
| expired because they have been either queued for too long or
| replaced by a newer report. When a report is considered
| expired, it may be considered for removal and, thus, never
| transmitted. This consideration is expected to be part of the
| implementation of the queuing device and not the responsibility
| of the reporting function within the DTNMA.
When reports are sent to a managing device over a challenged network,
they may arrive out of order due to taking different paths through
the network or being delayed due to retransmissions. A managing
device should not infer meaning from the order in which reports are
received.
Reports may or may not be associated with a specific Control. Some
reports may be annotated with the Control that caused the report to
be generated. Sometimes, a single report will represent the end
state of applying multiple Controls.
8.5. Authorization
Both local and remote services provided by the DTNMA affect the
behavior of multiple applications on a managed device and may
interface with multiple managing devices.
Authorization services enforce the potentially complex mapping of
other DTNMA services amongst managed and managing devices in the
network. For example, fine-grained access control can determine
which managing devices receive which reports, and what Controls can
be used to alter which managed applications.
This is particularly beneficial in networks that deal with either
multiple administrative entities or overlay networks that cross
administrative boundaries. Allowlists, blocklists, key-based
infrastructures, or other schemes may be used for this purpose.
9. Logical Autonomy Model
An important characteristic of the DTNMA is the shift in the role of
a managing device. One way to describe the behavior of the agent
autonomy engine is to describe the characteristics of the autonomy
model it implements.
This section describes a logical autonomy model in terms of the
abstract data elements that would comprise the model. Defining
abstract data elements allows for an unambiguous discussion of the
behavior of an autonomy model without mandating a particular design,
encoding, or transport associated with that model.
9.1. Overview
A managing autonomy capability on a potentially disconnected device
needs to behave in both an expressive and deterministic way.
Expressivity allows for the model to be configured for a wide range
of future situations. Determinism allows for the forensic
reconstruction of device behavior as part of debugging or recovery
efforts. It also is necessary to ensure predictable behavior.
| NOTE: The use of predicate logic and a stimulus-response system
| does not conflict with the use of higher-level autonomous
| functions or the incorporation of Machine Learning (ML).
| Specifically, the DTNMA deterministic autonomy model can
| coexist with other autonomous functions managing applications
| and network services.
|
| An example of such coexistence is the use of the DTNMA model to
| ensure that a device stays within safe operating parameters
| while a less deterministic ML model directs other behaviors for
| the device.
The DTNMA autonomy model is a rule-based model in which individual
rules associate a pre-identified stimulus with a preconfigured
response to that stimulus.
Stimuli are identified using one or more predicate logic expressions
that examine aspects of the state of the managed device. Responses
are implemented by running one or more procedures on the managed
device.
In its simplest form, a stimulus is a single predicate expression of
a condition that examines some aspect of the state of the managed
device. When the condition is met, a predetermined response is
applied. This behavior can be captured using the construct:
IF <condition 1> THEN <response 1>
In more complex forms, a stimulus may include both a common condition
shared by multiple rules and a specific condition for each individual
rule. If the common condition is not met, the evaluation of the
specific condition of each rule sharing the common condition can be
skipped. In this way, the total number of predicate evaluations can
be reduced. This behavior can be captured using the construct:
IF <common condition> THEN
IF <specific condition 1> THEN <response 1>
IF <specific condition 2> THEN <response 2>
IF <specific condition 3> THEN <response 3>
| NOTE: The DTNMA model remains a stimulus-response system,
| regardless of whether a common condition is part of the
| stimulus. However, it is recommended that implementations
| incorporate a common condition because of the efficiency
| provided by such a bulk evaluation.
|
| NOTE: One use of a stimulus "common condition" is to associate
| the condition with an onboard event such as the expiring of a
| timer or the changing of a monitored value.
The DTNMA does not prescribe when to evaluate rule stimuli.
Implementations may choose to evaluate rule stimuli at periodic
intervals (such as 1 Hz or 100 Hz). When stimuli include onboard
events, implementations may choose to perform an immediate evaluation
at the time of the event rather than waiting for a periodic
evaluation.
The flow of data into and out of the agent autonomy engine is
illustrated in Figure 3.
Managed Applications | DTNMA Agent | DTNMA Manager
+---------------------+--------------------------------+--------------+
| +---------+ |
| | Local | | Encoded
| | Rule DB |<-------------------- Policy
| +---------+ | Expressions
| ^ |
| | |
| v |
| +----------+ +---------+ |
Monitoring Data------>| Agent | | Runtime | |
| | Autonomy |<-->| Data- |<---- Definitions
Application Control<------| Engine | | store | |
| +----------+ +---------+ |
| | |
| +-------------------------> Reports
| |
Figure 3: DTNMA Autonomy Model
In the model shown in Figure 3, the autonomy engine stores the
combination of stimulus conditions and associated responses as a set
of "rules" in a rule database. This database is updated through the
execution of the autonomy engine and as configured from policy
statements received by DMs.
Stimuli are detected by examining the state of applications as
reported through application monitoring interfaces and through any
locally derived data. Local data is calculated in accordance with
definitions also provided by DMs as part of the runtime datastore.
Responses to stimuli may include updates to the rule database,
updates to the runtime datastore, Controls sent to applications, and
the generation of reports.
9.2. Model Characteristics
There are several practical challenges to the implementation of a
distributed rule-based system. Large numbers of rules may be
difficult to understand, deconflict, and debug. Rules whose
conditions are given by fused or other dynamic data may require data
logging and reporting for deterministic offline analysis. Rule
differences across managed devices may lead to oscillating effects.
This section identifies those characteristics of an autonomy model
that might help implementations mitigate some of these challenges.
There are a number of ways to represent data values, and many data
modeling languages exist for this purpose. When considering how to
model data in the context of the DTNMA autonomy model, there are some
modeling features that should be present to enable functionality.
There are also some modeling features that should be prevented to
avoid ambiguity.
Conventional network management approaches favor flexibility in their
data models. The DTNMA stresses deterministic behavior that supports
forensic analysis of agent activities "after the fact". As such, the
following statements should be true of all data representations
relating to DTNMA autonomy.
Strong Typing: The predicates and expressions that comprise the
autonomy services in the DTNMA should require strict data typing.
This avoids errors associated with implicit data conversions and
helps detect misconfigurations.
Acyclic Dependency: Many dependencies exist in an autonomy model,
particularly when combining individual expressions or results to
create complex behaviors. Implementations that conform to the
DTNMA need to prevent circular dependencies.
Fresh Data: Autonomy models operating on data values presume that
their data inputs represent the actionable state of the managed
device. If a data value has failed to be refreshed within a time
period, autonomy might incorrectly infer an operational state.
Regardless of whether a data value has changed, DTNMA
implementations should provide some indicator of whether the data
value is "fresh", i.e., meaning that it still represents the
current state of the device.
Pervasive Parameterization: Where possible, autonomy model objects
should support parameterization to allow for flexibility in the
specification. Parameterization allows for the definition of
fewer unique model objects and also can support the substitution
of local device state when exercising device control or data
reporting.
Configurable Cardinality: The number of data values that can be
supported in a given implementation is finite. For devices
operating in challenged environments, the number of supported
objects may be far fewer than the number of objects that can be
supported by devices in well-resourced environments. DTNMA
implementations should define limits to the number of supported
objects that can be active in a system at one time, as a function
of the resources available to the implementation.
Control-Based Updates: The agent autonomy engine changes the state
of the managed device by running Controls on the device. This is
different from approaches where the behavior of a managed device
is influenced by updating configuration values, such as in a table
or datastore. Altering behavior via one or more Controls allows
checking all preconditions before making changes as well as
providing more granularity in the way in which the device is
updated. Where necessary, Controls can be defined to perform bulk
updates of configuration data so as not to lose that update
modality. One important update precondition is that the system is
not performing an action that would prevent the update (such as
currently applying a competing update).
9.3. Data Value Representation
The expressive representation of simple data values is fundamental to
the successful construction and evaluation of predicates in the DTNMA
autonomy model. When defining such values, there are useful
distinctions regarding how values are identified and whether values
are generated in a way that is internal or external to the autonomy
model.
A DTNMA data value should combine a base type (e.g., integer, real,
string) representation with relevant semantic information. Base
types are used for proper storage and encoding. Semantic information
allows for additional typing, constraint definitions, and mnemonic
naming. This expanded definition of data values allows for better
predicate construction, better evaluation, and early type checking.
Data values may further be annotated based on whether their value is
the result of a DA calculation or the result of some external process
on the managed device. For example, operators may wish to know which
values can be updated by actions on the DA versus which values (such
as sensor readings) cannot be reliably changed because they are
calculated in a way that is external to the DA.
9.4. Data Reporting
The DTNMA autonomy model should, as required, report on the state of
its managed device (to include the state of the model itself). This
reporting should be done as a function of the changing state of the
managed device, independent of the connection to any managing device.
Queuing reports allows for later forensic analysis of device
behavior; this feature is a desirable property of DTNMA management.
DTNMA data reporting consists of the production of some data report
instance conforming to a data report schema. The use of schemas
allows a report instance to identify the schema to which it conforms
instead of carrying the structure in the report itself. This
approach can significantly reduce the size of generated reports.
The DTNMA data reporting concept is intentionally distinct from the
concept of exchanging datastores across a network. It is envisioned
that a DA might generate a data report instance of a data report
schema at regular intervals or in response to local events. In this
model, many report schemas may be defined to capture unique, relevant
combinations of known data values rather than sending bulk datastores
off-platform for analysis.
| NOTE: It is not required that data report schemas be tabular in
| nature. Individual implementations might define tabular
| schemas for table-like data and other report schemas for more
| heterogeneous reporting.
9.5. Command Execution
The agent autonomy engine requires that managed devices issue
commands on themselves as if they were otherwise being controlled by
a managing device. The DTNMA implements commanding through the use
of Controls and macros.
Controls represent parameterized, predefined procedures run by the DA
either as directed by the DM or as part of a rule response from the
DA autonomy engine. Macros represent ordered sequences of Controls.
Controls are conceptually similar to RPCs in that they represent
parameterized functions run on the managed device. However, they are
conceptually dissimilar to RPCs in that they do not have a concept of
a return code because they operate over an asynchronous transport.
The concept of a return code in an RPC implies a synchronous
relationship between the caller of the procedure and the procedure
being called, which might not be possible within the DTNMA.
The success or failure of a Control may be handled locally by the
agent autonomy engine. Local error handling is particularly
important in this architecture, given the potential for long periods
of disconnectivity between a DA and a DM. The failure of one or more
Controls is part of the state of the DA and can be used to trigger
rules within the DA autonomy engine.
The impact of a Control is externally observable via the generation
and eventual examination of data reports produced by the managed
device.
The failure of certain Controls might leave a managed device in an
undesirable state. Therefore, it is important that there be
consideration for Control-specific recovery mechanisms (such as a
rollback or safing mechanism). When a Control that is part of a
macro (such as in an autonomy response) fails, there may be a need to
implement a safe state for the managed device based on the nature of
the failure.
| NOTE: The use of the term "Control" in the DTNMA is derived in
| part from the concept of Command and Control (C2), where
| control implies the operational instructions undertaken to
| implement (or maintain) a commanded objective. The DA autonomy
| engine implements controls on a managed device to allow it to
| fulfill some commanded objective known by a (possibly
| disconnected) managing device.
|
| For example, a device might be commanded to maintain a safe
| internal thermal environment. Actions taken by a DA to manage
| heaters, louvers, and other temperature-affecting components
| are controls taken in service of that commanded objective.
9.6. Predicate Autonomy Rules
As discussed in Section 9.1, the DTNMA rule-based stimulus-response
system associates stimulus detection with a predetermined response.
Rules may be categorized based on whether (1) their stimuli include
generic statements of managed device state or (2) they are optimized
to only consider the passage of time on the device.
State-based rules are those whose stimulus is based on the evaluated
state of the managed device. Time-based rules are a unique subset of
state-based rules whose stimulus is given only by a time-based event.
Implementations might create different structures and evaluation
mechanisms for these two different types of rules to achieve more
efficient processing on a platform.
10. Use Cases
Using the autonomy model defined in Section 9, this section describes
flows through sample configurations conforming to the DTNMA. These
use cases illustrate remote configuration, local monitoring and
control, support for multiple DMs, and data fusion.
10.1. Notation
The use cases presented in this section are documented with a
shorthand notation to describe the types of data sent between
managers and agents. This notation, outlined in Table 1, leverages
the definitions of the autonomy model components defined in
Section 9.
+==============+=======================================+===========+
| Term | Definition | Example |
+==============+=======================================+===========+
| EDD# | Externally Defined Data -- a data | EDD1, |
| | value defined in a way that is | EDD2 |
| | external to the DA. | |
+--------------+---------------------------------------+-----------+
| V# | Variable -- a data value defined in a | V1 = EDD1 |
| | way that is internal to the DA. | + 7 |
+--------------+---------------------------------------+-----------+
| EXPR | Predicate expression -- used to | V1 > 5 |
| | define a rule stimulus. | |
+--------------+---------------------------------------+-----------+
| ID | DTNMA Object Identifier. | V1, EDD2 |
+--------------+---------------------------------------+-----------+
| ACL# | Enumerated Access Control List. | ACL1 |
+--------------+---------------------------------------+-----------+
| DEF(ACL, ID, | Define "ID" from expression. Allow | DEF(ACL1, |
| EXPR) | DMs in ACL to see this ID. | V1, EDD1 |
| | | + EDD2) |
+--------------+---------------------------------------+-----------+
| PROD(P, ID) | Produce "ID" according to predicate | PROD(1s, |
| | P. P may be a time period (1 second, | EDD1) |
| | or 1s) or an expression (EDD1 > 10). | |
+--------------+---------------------------------------+-----------+
| RPT(ID) | A report instance containing data | RPT(EDD1) |
| | named "ID". | |
+--------------+---------------------------------------+-----------+
Table 1: Terminology
These notations do not imply any implementation approach. They only
provide a succinct syntax for expressing the data flows in the use
case diagrams in the remainder of this section.
10.2. Serialized Management
This nominal configuration shows a single DM interacting with
multiple DAs. The control flow for this scenario is outlined in
Figure 4.
+-----------+ +---------+ +---------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Agent B |
+----+------+ +----+----+ +----+----+
| | |
|-----PROD(1s, EDD1)--->| | (1)
|----------------------------PROD(1s, EDD1)-->|
| | |
| | |
|<-------RPT(EDD1)------| | (2)
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------------------RPT(EDD1)-------|
| | |
Figure 4: Serialized Management Control Flow
In a serialized management scenario, a single DM interacts with
multiple DAs.
In this figure, DM A sends a policy to DAs A and B to report the
value of an EDD (EDD1) every second (step 1). Each DA receives this
policy and configures their respective autonomy engines for this
production. Thereafter (step 2), each DA produces a report
containing data element EDD1; each such report is then sent back to
the DM.
This behavior continues without any additional communications from
the DM.
10.3. Intermittent Connectivity
Building on the nominal configuration discussed in Section 10.2, this
scenario shows a challenged network in which connectivity between DA
B and the DM is temporarily lost. The control flow for this case is
outlined in Figure 5.
+-----------+ +---------+ +---------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Agent B |
+----+------+ +----+----+ +----+----+
| | |
|-----PROD(1s, EDD1)--->| | (1)
|----------------------------PROD(1s, EDD1)-->|
| | |
| | |
|<-------RPT(EDD1)------| | (2)
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|<-------RPT(EDD1)------| |
| | RPT(EDD1)| (3)
| | |
| | |
|<-------RPT(EDD1)------| |
| | RPT(EDD1)| (4)
| | |
| | |
|<-------RPT(EDD1)------| |
|<----------------RPT(EDD1), RPT(EDD1)--------| (5)
| | |
Figure 5: Challenged Management Control Flow
In a challenged network, DAs store reports, pending a transmit
opportunity.
In this figure, DM A sends a policy to DAs A and B to produce an EDD
(EDD1) every second (step 1). Each DA receives this policy and
configures their respective autonomy engines for this production.
Produced reports are transmitted when there is connectivity between
the DA and DM (step 2).
At some point, DA B loses the ability to transmit in the network
(steps 3 and 4). During this time period, DA B continues to produce
reports, but they are queued for transmission. This queuing might be
done by the DA itself or by a supporting transport such as BP.
Eventually (and before the next scheduled production of EDD1), DA B
is able to transmit in the network again (step 5), and all queued
reports are sent at that time. DA A maintains connectivity with the
DM during steps 3-5 and continues to send reports as they are
generated.
10.4. Open-Loop Reporting
This scenario illustrates the DTNMA open-loop control paradigm, where
DAs manage themselves in accordance with policies provided by DMs and
provide reports to DMs based on these policies.
The control flow shown in Figure 6 includes an example of data
fusion, where multiple policies configured by a DM result in a single
report from a DA.
+-----------+ +---------+ +---------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Agent B |
+----+------+ +----+----+ +----+----+
| | |
|-----PROD(1s, EDD1)--->| | (1)
|----------------------------PROD(1s, EDD1)-->|
| | |
| | |
|<-------RPT(EDD1)------| | (2)
|<----------------------------RPT(EDD1)-------|
| | |
| | |
|----------------------------PROD(1s, EDD2)-->| (3)
| | |
| | |
|<-------RPT(EDD1)------| |
|<-------------------------RPT(EDD1, EDD2)----| (4)
| | |
| | |
|<-------RPT(EDD1)------| |
|<-------------------------RPT(EDD1, EDD2)----|
| | |
Figure 6: Consolidated Management Control Flow
A many-to-one mapping between management policy and device state
reporting is supported by the DTNMA.
In this figure, DM A sends a policy statement in the form of a rule
to DAs A and B, which instructs the DAs to produce a report for EDD1
every second (step 1). Each DA receives this policy, which is stored
in its respective rule database, and configures its autonomy engine.
Reports are transmitted by each DA when produced (step 2).
At a later time, DM A sends an additional policy to DA B, requesting
the production of a report for EDD2 every second (step 3). This
policy is added to DA B's rule database.
Following this policy update, DA A will continue to produce EDD1, and
DA B will produce both EDD1 and EDD2 (step 4). However, DA B may
provide these values to the DM in a single report rather than as two
independent reports. In this way, there is no direct mapping between
the consolidated reports sent by DA B (from step 4 onwards) and the
two different policies sent to DA B (steps 1 and 3) that produce the
information included in those consolidated reports.
10.5. Multiple Administrative Domains
The managed applications on a DA may be controlled by different
administrative entities in a network. The DTNMA allows DAs to
communicate with multiple DMs in the network, such as in cases where
there is one DM per administrative domain.
Whenever a DM sends a policy expression to a DA, that policy
expression may be associated with authorization information. One
method of representing this is an ACL.
The use of an ACL in this use case does not imply that the DTNMA
requires ACLs to annotate policy expressions. ACLs and their
representation in this context are for example purposes only.
The ability of one DM to access the results of policy expressions
configured by some other DM will be limited to the authorization
annotations of those policy expressions.
An example of multi-manager authorization is illustrated in Figure 7.
+-----------+ +---------+ +-----------+
| DTNMA | | DTNMA | | DTNMA |
| Manager A | | Agent A | | Manager B |
+-----+-----+ +----+----+ +-----+-----+
| | |
|--DEF(ACL1, V1, EDD1*2)-->|<---DEF(ACL2, V2, EDD2*2)---| (1)
| | |
|---PROD(1s, V1)---------->|<---PROD(1s, V2)------------| (2)
| | |
|<--------RPT(V1)----------| | (3)
| |--------RPT(V2)------------>|
|<--------RPT(V1)----------| |
| |--------RPT(V2)------------>|
| | |
| |<---PROD(1s, V1)------------| (4)
| | |
| |---ERR(V1 not permitted)--->|
| | |
|--DEF(NULL, V3, EDD3*3)-->| | (5)
| | |
|---PROD(1s, V3)---------->| | (6)
| | |
| |<----PROD(1s, V3)-----------|
| | |
|<--------RPT(V3)----------|--------RPT(V3)------------>| (7)
|<--------RPT(V1)----------| |
| |--------RPT(V2)------------>|
|<-------RPT(V3)-----------|--------RPT(V3)------------>|
|<-------RPT(V1)-----------| |
| |--------RPT(V2)------------>|
Figure 7: Multiplexed Management Control Flow
Multiple DMs may interface with a single DA, particularly in complex
networks.
In this figure, both DM A and DM B send policies to DA A (step 1).
DM A defines a variable (V1) whose value is given by the mathematical
expression (EDD1 * 2) and is associated with an ACL (ACL1) that
restricts access to V1 to DM A only. Similarly, DM B defines a
variable (V2) whose value is given by the mathematical expression
(EDD2 * 2) and is associated with an ACL (ACL2) that restricts access
to V2 to DM B only.
Both DM A and DM B also send policies to DA A to report on the values
of their variables at 1-second intervals (step 2). Since DM A can
access V1 and DM B can access V2, there is no authorization issue
with these policies, and they are both accepted by the autonomy
engine on DA A. DA A produces reports as expected, sending them to
their respective managers (step 3).
Later (step 4), DM B attempts to configure DA A to also report to it
the value of V1. Since DM B does not have authorization to view this
variable, DA A does not include this in the configuration of its
autonomy engine; instead, some indication of a permission error is
included in any regular reporting back to DM B.
DM A also sends a policy to DA A (step 5) that defines a variable
(V3) whose value is given by the mathematical expression (EDD3 * 3)
and is not associated with an ACL, indicating that any DM can access
V3. In this instance, both DM A and DM B can then send policies to
DA A to report the value of V3 (step 6). Since there is no
authorization restriction on V3, these policies are accepted by the
autonomy engine on DA A, and reports are sent to both DM A and DM B
over time (step 7).
10.6. Cascading Management
There are times when a single network device may serve as both a DM
for other DAs in the network and, itself, as a device managed by
someone else. This may be the case on nodes serving as gateways or
proxies. The DTNMA accommodates this case by allowing a single
device to run both a DA and a DM.
An example of this configuration is illustrated in Figure 8.
---------------------------------------
| Node B |
| |
+-----------+ | +-----------+ +---------+ | +---------+
| DTNMA | | | DTNMA | | DTNMA | | | DTNMA |
| Manager A | | | Manager B | | Agent B | | | Agent C |
+---+-------+ | +-----+-----+ +----+----+ | +----+----+
| | | | | |
|----------DEF(NULL, V0, EDD1 + EDD2)-->| | | (1)
|-------------PROD(1s, V0)------------->| | |
| | | | | |
| | |-PROD(1s, EDD1)-->| | | (2)
| | |--------------------PROD(1s, EDD2)-->| (2)
| | | | | |
| | | | | |
| | |<----RPT(EDD1)----| | | (3)
| | |<--------------------RPT(EDD2)-------| (3)
| | | | | |
|<-------------RPT(V0)------------------| | | (4)
| | | | | |
| | | | | |
| |
| |
---------------------------------------
Figure 8: Cascading Management Control Flow
A device can operate as both a DM and a DA.
In this example, we presume that DA B is able to sample a given EDD
(EDD1) and that DA C is able to sample a different EDD (EDD2). Node
B houses DM B (which controls DA C) and DA B (which is controlled by
DM A). DM A must periodically receive some new value that is
calculated as a function of both EDD1 and EDD2.
First, DM A sends a policy to DA B to define a variable (V0) whose
value is given by the mathematical expression (EDD1 + EDD2) without a
restricting ACL. Further, DM A sends a policy to DA B to report on
the value of V0 every second (step 1).
DA B needs the ability to monitor both EDD1 and EDD2 to produce V0.
DA B is able to sample EDD1, so DM B sends a policy to DA B to report
on the value of EDD1. However, the only way to receive EDD2 values
is to have them reported back to Node B by DA C and included in the
Node B runtime datastores. Therefore, DM B also sends a policy to DA
C to report on the value of EDD2 (step 2).
DA B receives the policy in its autonomy engine and produces reports
on the value of EDD2 every second. Similarly, DA C receives the
policy in its autonomy engine and produces reports on the value of
EDD2 every second (step 3).
DA B may locally sample EDD1 and EDD2 and uses that to compute values
of V0 and report on those values at regular intervals to DM A (step
4).
While a trivial example, the mechanism of associating fusion with the
DA function rather than the DM function scales with fusion
complexity. Within the DTNMA, DAs and DMs are not required to be
separate software implementations. There may be a single software
application running on Node B implementing both DM B and DA B roles.
11. IANA Considerations
This document has no IANA actions.
12. Security Considerations
Security within a DTNMA exists in at least the following two layers:
security in the data model and security in the messaging and encoding
of the data model.
Data model security refers to the validity and accessibility of data
elements. For example, a data element might be available to certain
DAs or DMs in a system, whereas the same data element may be hidden
from other DAs or DMs. Both verification and authorization
mechanisms at DAs and DMs are important to achieve this type of
security.
| NOTE: One way to provide finer-grained application security is
| through the use of ACLs that would be defined as part of the
| configuration of DAs and DMs. It is expected that many common
| data model tools provide mechanisms for the definition of ACLs
| and best practices for their operational use.
The exchange of information between and amongst DAs and DMs in the
DTNMA is expected to be accomplished through some secured messaging
transport.
13. Informative References
[ASN.1] ITU-T, "Information technology - Abstract Syntax Notation
One (ASN.1): Specification of basic notation", ITU-T
Recommendation X.680, ISO/IEC 8824-1:2021, February 2021,
<https://www.itu.int/rec/T-REC-X.680>.
[CORE-COMI]
Veillette, M., Ed., van der Stok, P., Ed., Pelov, A., Ed.,
Bierman, A., and C. Bormann, Ed., "CoAP Management
Interface (CORECONF)", Work in Progress, Internet-Draft,
draft-ietf-core-comi-19, 3 November 2024,
<https://datatracker.ietf.org/doc/html/draft-ietf-core-
comi-19>.
[DART] Tropf, B. T., Haque, M., Behrooz, N., and C. Krupiarz,
"The DART Autonomy System", DOI 10.1109/SMC-
IT56444.2023.00020, August 2023,
<https://ieeexplore.ieee.org/abstract/document/10207457>.
[gNMI] Borman, P., Hines, M., Lebsack, C., Morrow, C., Shaikh,
A., Shakir, R., Li, W., and D. Loher, "gRPC Network
Management Interface (gNMI)", Version 10.0, May 2023,
<https://www.openconfig.net/docs/gnmi/gnmi-
specification/>.
[gRPC] gRPC Authors, "gRPC Documentation", 2024,
<https://grpc.io/docs/>.
[IPMI] Intel, Hewlett-Packard, NEC, and Dell, "Intelligent
Platform Management Interface Specification, Second
Generation", Version 2.0, October 2013,
<https://www.intel.la/content/dam/www/public/us/en/
documents/specification-updates/ipmi-intelligent-platform-
mgt-interface-spec-2nd-gen-v2-0-spec-update.pdf>.
[NEW-HORIZONS]
Moore, R. C., "Autonomous safeing and fault protection for
the New Horizons mission to Pluto", Acta Astronautica,
Volume 61, Issues 1-6, June-August 2007, Pages 398-405,
DOI 10.1016/j.actaastro.2007.01.009, August 2007,
<https://www.sciencedirect.com/science/article/pii/
S0094576507000604>.
[PROTOCOL-BUFFERS]
Stuart, S. and R. Fernando, "Encoding rules and MIME type
for Protocol Buffers", Work in Progress, Internet-Draft,
draft-rfernando-protocol-buffers-00, 8 October 2012,
<https://datatracker.ietf.org/doc/html/draft-rfernando-
protocol-buffers-00>.
[RFC 2578] McCloghrie, K., Ed., Perkins, D., Ed., and J.
Schoenwaelder, Ed., "Structure of Management Information
Version 2 (SMIv2)", STD 58, RFC 2578,
DOI 10.17487/RFC 2578, April 1999,
<https://www.rfc-editor.org/info/RFC 2578>.
[RFC 2982] Kavasseri, R., Ed., "Distributed Management Expression
MIB", RFC 2982, DOI 10.17487/RFC 2982, October 2000,
<https://www.rfc-editor.org/info/RFC 2982>.
[RFC 3165] Levi, D. and J. Schoenwaelder, "Definitions of Managed
Objects for the Delegation of Management Scripts",
RFC 3165, DOI 10.17487/RFC 3165, August 2001,
<https://www.rfc-editor.org/info/RFC 3165>.
[RFC 3410] Case, J., Mundy, R., Partain, D., and B. Stewart,
"Introduction and Applicability Statements for Internet-
Standard Management Framework", RFC 3410,
DOI 10.17487/RFC 3410, December 2002,
<https://www.rfc-editor.org/info/RFC 3410>.
[RFC 3411] Harrington, D., Presuhn, R., and B. Wijnen, "An
Architecture for Describing Simple Network Management
Protocol (SNMP) Management Frameworks", STD 62, RFC 3411,
DOI 10.17487/RFC 3411, December 2002,
<https://www.rfc-editor.org/info/RFC 3411>.
[RFC 3414] Blumenthal, U. and B. Wijnen, "User-based Security Model
(USM) for version 3 of the Simple Network Management
Protocol (SNMPv3)", STD 62, RFC 3414,
DOI 10.17487/RFC 3414, December 2002,
<https://www.rfc-editor.org/info/RFC 3414>.
[RFC 3416] Presuhn, R., Ed., "Version 2 of the Protocol Operations
for the Simple Network Management Protocol (SNMP)",
STD 62, RFC 3416, DOI 10.17487/RFC 3416, December 2002,
<https://www.rfc-editor.org/info/RFC 3416>.
[RFC 3417] Presuhn, R., Ed., "Transport Mappings for the Simple
Network Management Protocol (SNMP)", STD 62, RFC 3417,
DOI 10.17487/RFC 3417, December 2002,
<https://www.rfc-editor.org/info/RFC 3417>.
[RFC 3418] Presuhn, R., Ed., "Management Information Base (MIB) for
the Simple Network Management Protocol (SNMP)", STD 62,
RFC 3418, DOI 10.17487/RFC 3418, December 2002,
<https://www.rfc-editor.org/info/RFC 3418>.
[RFC 4838] Cerf, V., Burleigh, S., Hooke, A., Torgerson, L., Durst,
R., Scott, K., Fall, K., and H. Weiss, "Delay-Tolerant
Networking Architecture", RFC 4838, DOI 10.17487/RFC 4838,
April 2007, <https://www.rfc-editor.org/info/RFC 4838>.
[RFC 4949] Shirey, R., "Internet Security Glossary, Version 2",
FYI 36, RFC 4949, DOI 10.17487/RFC 4949, August 2007,
<https://www.rfc-editor.org/info/RFC 4949>.
[RFC 5591] Harrington, D. and W. Hardaker, "Transport Security Model
for the Simple Network Management Protocol (SNMP)",
STD 78, RFC 5591, DOI 10.17487/RFC 5591, June 2009,
<https://www.rfc-editor.org/info/RFC 5591>.
[RFC 5592] Harrington, D., Salowey, J., and W. Hardaker, "Secure
Shell Transport Model for the Simple Network Management
Protocol (SNMP)", RFC 5592, DOI 10.17487/RFC 5592, June
2009, <https://www.rfc-editor.org/info/RFC 5592>.
[RFC 5652] Housley, R., "Cryptographic Message Syntax (CMS)", STD 70,
RFC 5652, DOI 10.17487/RFC 5652, September 2009,
<https://www.rfc-editor.org/info/RFC 5652>.
[RFC 6241] Enns, R., Ed., Bjorklund, M., Ed., Schoenwaelder, J., Ed.,
and A. Bierman, Ed., "Network Configuration Protocol
(NETCONF)", RFC 6241, DOI 10.17487/RFC 6241, June 2011,
<https://www.rfc-editor.org/info/RFC 6241>.
[RFC 6242] Wasserman, M., "Using the NETCONF Protocol over Secure
Shell (SSH)", RFC 6242, DOI 10.17487/RFC 6242, June 2011,
<https://www.rfc-editor.org/info/RFC 6242>.
[RFC 6353] Hardaker, W., "Transport Layer Security (TLS) Transport
Model for the Simple Network Management Protocol (SNMP)",
STD 78, RFC 6353, DOI 10.17487/RFC 6353, July 2011,
<https://www.rfc-editor.org/info/RFC 6353>.
[RFC 6991] Schoenwaelder, J., Ed., "Common YANG Data Types",
RFC 6991, DOI 10.17487/RFC 6991, July 2013,
<https://www.rfc-editor.org/info/RFC 6991>.
[RFC 7228] Bormann, C., Ersue, M., and A. Keranen, "Terminology for
Constrained-Node Networks", RFC 7228,
DOI 10.17487/RFC 7228, May 2014,
<https://www.rfc-editor.org/info/RFC 7228>.
[RFC 7252] Shelby, Z., Hartke, K., and C. Bormann, "The Constrained
Application Protocol (CoAP)", RFC 7252,
DOI 10.17487/RFC 7252, June 2014,
<https://www.rfc-editor.org/info/RFC 7252>.
[RFC 7575] Behringer, M., Pritikin, M., Bjarnason, S., Clemm, A.,
Carpenter, B., Jiang, S., and L. Ciavaglia, "Autonomic
Networking: Definitions and Design Goals", RFC 7575,
DOI 10.17487/RFC 7575, June 2015,
<https://www.rfc-editor.org/info/RFC 7575>.
[RFC 7589] Badra, M., Luchuk, A., and J. Schoenwaelder, "Using the
NETCONF Protocol over Transport Layer Security (TLS) with
Mutual X.509 Authentication", RFC 7589,
DOI 10.17487/RFC 7589, June 2015,
<https://www.rfc-editor.org/info/RFC 7589>.
[RFC 7950] Bjorklund, M., Ed., "The YANG 1.1 Data Modeling Language",
RFC 7950, DOI 10.17487/RFC 7950, August 2016,
<https://www.rfc-editor.org/info/RFC 7950>.
[RFC 7951] Lhotka, L., "JSON Encoding of Data Modeled with YANG",
RFC 7951, DOI 10.17487/RFC 7951, August 2016,
<https://www.rfc-editor.org/info/RFC 7951>.
[RFC 8040] Bierman, A., Bjorklund, M., and K. Watsen, "RESTCONF
Protocol", RFC 8040, DOI 10.17487/RFC 8040, January 2017,
<https://www.rfc-editor.org/info/RFC 8040>.
[RFC 8199] Bogdanovic, D., Claise, B., and C. Moberg, "YANG Module
Classification", RFC 8199, DOI 10.17487/RFC 8199, July
2017, <https://www.rfc-editor.org/info/RFC 8199>.
[RFC 8294] Liu, X., Qu, Y., Lindem, A., Hopps, C., and L. Berger,
"Common YANG Data Types for the Routing Area", RFC 8294,
DOI 10.17487/RFC 8294, December 2017,
<https://www.rfc-editor.org/info/RFC 8294>.
[RFC 8341] Bierman, A. and M. Bjorklund, "Network Configuration
Access Control Model", STD 91, RFC 8341,
DOI 10.17487/RFC 8341, March 2018,
<https://www.rfc-editor.org/info/RFC 8341>.
[RFC 8342] Bjorklund, M., Schoenwaelder, J., Shafer, P., Watsen, K.,
and R. Wilton, "Network Management Datastore Architecture
(NMDA)", RFC 8342, DOI 10.17487/RFC 8342, March 2018,
<https://www.rfc-editor.org/info/RFC 8342>.
[RFC 8368] Eckert, T., Ed. and M. Behringer, "Using an Autonomic
Control Plane for Stable Connectivity of Network
Operations, Administration, and Maintenance (OAM)",
RFC 8368, DOI 10.17487/RFC 8368, May 2018,
<https://www.rfc-editor.org/info/RFC 8368>.
[RFC 8639] Voit, E., Clemm, A., Gonzalez Prieto, A., Nilsen-Nygaard,
E., and A. Tripathy, "Subscription to YANG Notifications",
RFC 8639, DOI 10.17487/RFC 8639, September 2019,
<https://www.rfc-editor.org/info/RFC 8639>.
[RFC 8641] Clemm, A. and E. Voit, "Subscription to YANG Notifications
for Datastore Updates", RFC 8641, DOI 10.17487/RFC 8641,
September 2019, <https://www.rfc-editor.org/info/RFC 8641>.
[RFC 8990] Bormann, C., Carpenter, B., Ed., and B. Liu, Ed., "GeneRic
Autonomic Signaling Protocol (GRASP)", RFC 8990,
DOI 10.17487/RFC 8990, May 2021,
<https://www.rfc-editor.org/info/RFC 8990>.
[RFC 8993] Behringer, M., Ed., Carpenter, B., Eckert, T., Ciavaglia,
L., and J. Nobre, "A Reference Model for Autonomic
Networking", RFC 8993, DOI 10.17487/RFC 8993, May 2021,
<https://www.rfc-editor.org/info/RFC 8993>.
[RFC 9113] Thomson, M., Ed. and C. Benfield, Ed., "HTTP/2", RFC 9113,
DOI 10.17487/RFC 9113, June 2022,
<https://www.rfc-editor.org/info/RFC 9113>.
[RFC 9171] Burleigh, S., Fall, K., and E. Birrane, III, "Bundle
Protocol Version 7", RFC 9171, DOI 10.17487/RFC 9171,
January 2022, <https://www.rfc-editor.org/info/RFC 9171>.
[RFC 9172] Birrane, III, E. and K. McKeever, "Bundle Protocol
Security (BPSec)", RFC 9172, DOI 10.17487/RFC 9172, January
2022, <https://www.rfc-editor.org/info/RFC 9172>.
[RFC 9254] Veillette, M., Ed., Petrov, I., Ed., Pelov, A., Bormann,
C., and M. Richardson, "Encoding of Data Modeled with YANG
in the Concise Binary Object Representation (CBOR)",
RFC 9254, DOI 10.17487/RFC 9254, July 2022,
<https://www.rfc-editor.org/info/RFC 9254>.
[RFC 9595] Veillette, M., Ed., Pelov, A., Ed., Petrov, I., Ed.,
Bormann, C., and M. Richardson, "YANG Schema Item
iDentifier (YANG SID)", RFC 9595, DOI 10.17487/RFC 9595,
July 2024, <https://www.rfc-editor.org/info/RFC 9595>.
[xml-infoset]
Cowan, J., Ed. and R. Tobin, Ed., "XML Information Set
(Second Edition)", W3C Recommendation REC-xml-infoset-
20040204, February 2004,
<https://www.w3.org/TR/2004/REC-xml-infoset-20040204/>.
[XPath] Robie, J., Ed., Dyck, M., Ed., and J. Spiegel, Ed., "XML
Path Language (XPath) 3.1", March 2017,
<https://www.w3.org/TR/2017/REC-xpath-31-20170321/>.
Latest version available at
<https://www.w3.org/TR/xpath-31/>.
Acknowledgements
Brian Sipos of the Johns Hopkins University Applied Physics
Laboratory (JHU/APL) provided excellent technical review of the DTNMA
concepts presented in this document and additional information
related to existing network management techniques.
Authors' Addresses
Edward J. Birrane, III
The Johns Hopkins University Applied Physics Laboratory
Email: Edward.Birrane@jhuapl.edu
Sarah Heiner
The Johns Hopkins University Applied Physics Laboratory
Email: Sarah.Heiner@jhuapl.edu
Emery Annis
The Johns Hopkins University Applied Physics Laboratory
Email: Emery.Annis@jhuapl.edu
RFC TOTAL SIZE: 133898 bytes
PUBLICATION DATE: Thursday, November 7th, 2024
LEGAL RIGHTS: The IETF Trust (see BCP 78)
|
