Internet Research Task Force (IRTF) M. Behringer Request for Comments: 7575 M. Pritikin Category: Informational S. Bjarnason ISSN: 2070-1721 A. Clemm Cisco Systems B. Carpenter Univ. of Auckland S. Jiang Huawei Technologies Co., Ltd L. Ciavaglia Alcatel Lucent June 2015
Autonomic Networking: Definitions and Design Goals
Abstract
Autonomic systems were first described in 2001. The fundamental goal is self-management, including self-configuration, self-optimization, self-healing, and self-protection. This is achieved by an autonomic function having minimal dependencies on human administrators or centralized management systems. It usually implies distribution across network elements.
This document defines common language and outlines design goals (and what are not design goals) for autonomic functions. A high-level reference model illustrates how functional elements in an Autonomic Network interact. This document is a product of the IRTF's Network Management Research Group.
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 Research Task Force (IRTF). The IRTF publishes the results of Internet-related research and development activities. These results might not be suitable for deployment. This RFC represents the consensus of the Network Management Research Group of the Internet Research Task Force (IRTF). Documents approved for publication by the IRSG are not a candidate for any level of Internet Standard; see Section 2 of RFC 5741.
Information about the current status of this document, any errata, and how to provide feedback on it may be obtained at http://www.rfc-editor.org/info/rfc7575.
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Copyright Notice
Copyright (c) 2015 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 (http://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.
Autonomic systems were first described in a manifesto by IBM in 2001 [Kephart]. The fundamental concept involves eliminating external systems from a system's control loops and closing of control loops within the autonomic system itself, with the goal of providing the system with self-management capabilities, including self- configuration, self-optimization, self-healing, and self-protection.
IP networking was initially designed with similar properties in mind. An IP network should be distributed and redundant to withstand outages in any part of the network. Routing protocols such as OSPF and IS-IS exhibit properties of self-management and can thus be considered autonomic in the definition of this document.
However, as IP networking evolved, the ever-increasing intelligence of network elements was often not put into protocols to follow this paradigm, but was put into external configuration systems. This configuration made network elements dependent on some process that manages them, either a human or a network management system.
Autonomic functions can be defined in two ways:
o On a node level: Nodes interact with each other to form feedback loops.
o On a system level: Feedback loops include central elements as well.
System-level autonomy is implicitly or explicitly the subject in many IETF working groups, where interactions with controllers or network management systems are discussed.
This work specifically focuses on node-level autonomic functions. It focuses on intelligence of algorithms at the node level, to minimize dependency on human administrators and central management systems.
Some network deployments benefit from a fully autonomic approach, for example, networks with a large number of relatively simple devices. Most currently deployed networks, however, will require a mixed approach, where some functions are autonomic and others are centrally managed. Central management of networking functions clearly has advantages and will be chosen for many networking functions. This document does not discuss which functions should be centralized or follow an autonomic approach. Instead, it should help make the decision which is the best approach for a given situation.
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Autonomic function cannot always discover all required information; for example, policy-related information requires human input, because policy is by its nature derived and specified by humans. Where input from some central intelligence is required, it is provided in a highly abstract, network-wide form.
Autonomic Computing in general and Autonomic Networking in particular have been the subject of academic study for many years. There is much literature, including several useful overview papers (e.g., [Samaan], [Movahedi], and [Dobson]). In the present document, we focus on concepts and definitions that seem sufficiently mature to become the basis for interoperable specifications in the near future. In particular, such specifications will need to coexist with traditional methods of network configuration and management, rather than realizing an exclusively autonomic system with all the properties that it would require.
There is an important difference between "automatic" and "autonomic". "Automatic" refers to a predefined process, such as a script. "Autonomic" is used in the context of self-management. It includes feedback loops between elements as well as northbound to central elements. See also the definitions in the next section. Generally, an automatic process works in a given environment but has to be adapted if the environment changes. An autonomic process can adapt to changing environments.
This document provides the definitions and design goals for Autonomic Networking in the IETF and IRTF. It represents the consensus of the IRTF's Network Management Research Group (NMRG).
Autonomic: Self-managing (self-configuring, self-protecting, self- healing, self-optimizing); however, allowing high-level guidance by a central entity, through Intent (see below). An autonomic function adapts on its own to a changing environment.
Automatic: A process that occurs without human intervention, with step-by-step execution of rules. However, it relies on humans defining the sequence of rules, so is not Autonomic in the full sense. For example, a start-up script is automatic but not autonomic. An automatic function may need manual adjustments if the environment changes.
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Intent: An abstract, high-level policy used to operate the network. Its scope is an autonomic domain, such as an enterprise network. It does not contain configuration or information for a specific node (see Section 3.2 on how Intent coexists with alternative management paradigms). It may contain information pertaining to a node with a specific role (for example, an edge switch) or a node running a specific function. Intent is typically defined and provided by a central entity.
Autonomic Domain: A collection of autonomic nodes that instantiate the same Intent.
Autonomic Function: A feature or function that requires no configuration and can derive all required information through self- knowledge, discovery, or Intent.
Autonomic Service Agent: An agent implemented on an autonomic node that implements an autonomic function, either in part (in the case of a distributed function) or whole.
Autonomic Node: A node that employs exclusively autonomic functions. It requires (!) no configuration. (Note that configuration can be used to override an autonomic function. See Section 3.2 for more details.) An Autonomic Node may operate on any layer of the networking stack. Examples are routers, switches, personal computers, call managers, etc.
Autonomic Network: A network containing exclusively autonomic nodes. It may contain one or several autonomic domains.
The original design goals of autonomic systems as described in [Kephart] also apply to Autonomic Networks. The overarching goal is self-management, which is comprised of several "self" properties. The most commonly cited are:
o Self-configuration: Functions do not require configuration, by either an administrator or a management system. They configure themselves, based on self-knowledge, discovery, and Intent. Discovery is the default way for an autonomic function to receive the information it needs to operate.
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o Self-healing: Autonomic functions adapt on their own to changes in the environment and heal problems automatically.
o Self-optimizing: Autonomic functions automatically determine ways to optimize their behavior against a set of well-defined goals.
o Self-protection: Autonomic functions automatically secure themselves against potential attacks.
Almost any network can be described as "self-managing", as long as the definition of "self" is large enough. For example, a well- defined Software-Defined Networking (SDN) system, including the controller elements, can be described overall as "autonomic", if the controller provides an interface to the administrator that has the same properties as mentioned above (high level, network-wide, etc.).
For the work in the IETF and IRTF, we define the "self" properties on the node level. It is the design goal to make functions on network nodes self-managing, in other words, minimally dependent on management systems or controllers, as well as human operators. Self- managing functions on a node might need to exchange information with other nodes in order to achieve this design goal.
As mentioned in the introduction, closed-loop control is an important aspect of self-managing systems. This implies peer-to-peer dialogues between the parties that make up the closed loop. Such dialogues require two-way "discussion" or "negotiation" between each pair or groups of peers involved in the loop, so they cannot readily use typical top-down command-response protocols. Also, a discovery phase is unavoidable before such closed-loop control can take place. Multiparty protocols are also possible but can be significantly more complex.
For the foreseeable future, autonomic nodes and networks will be the exception; autonomic behavior will initially be defined function by function. Therefore, coexistence with other network management paradigms has to be considered. Examples are management by command line, SNMP, SDN (with related APIs), the Network Configuration Protocol (NETCONF), etc.
Conflict resolution between a) autonomic default behavior and Intent and b) other methods is therefore required. This is achieved through prioritization. Generally, autonomic mechanisms define a network- wide behavior, whereas the alternative methods are typically on a node-by-node basis. Node-based management concepts take a higher priority over autonomic methods. This is in line with current
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examples of autonomic functions; for example, with routing, a (statically configured) route has priority over the routing algorithm. In short:
o lowest priority: autonomic default behavior
o medium priority: autonomic Intent
o highest priority: node-specific network management concepts, such as command line, SNMP, SDN, NETCONF, etc. How these concepts are prioritized is outside the scope of this document.
The above prioritization essentially results in the actions of the human administrator always being able to overrule autonomic behavior. This is generally the expectation of network operators today and therefore remains a design principle here. In critical systems, such as atomic power plants, sometimes the opposite philosophy is used: The expectation is that a well-defined algorithm is more reliable than a human operator, especially in rare exception cases. Networking generally does not follow this philosophy yet. However, warnings should be issued if node-specific overrides may conflict with autonomic behavior.
In other fields, autonomic mechanisms disengage automatically if certain conditions occur: The autopilot in a plane switches off if the plane is outside a predefined envelope of flight parameters. The assumption is that the algorithms only work correctly if the input values are in expected ranges. However, some opinions suggest that exactly in exceptional conditions is the worst moment to switch off autonomic behavior, since the pilots have no full understanding of the situation at this point and may be under high levels of stress. For this reason, we suggest here to NOT generally disable autonomic functions if they encounter unexpected conditions, because it is expected that this adds another level of unpredictability in networks, when the situation may already be hard to understand.
All autonomic interactions should be secure by default. This requires that any member of an autonomic domain can assert its membership using a domain identity, for example, a certificate issued by a domain certification authority. This domain identity is used for nodes to learn about their neighboring nodes, to determine the boundaries of the domain, and to cryptographically secure interactions within the domain. Nodes from different domains can also mutually verify their identity and secure interactions as long as they have a mutually respected trust anchor.
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A strong, cryptographically verifiable domain identity is a fundamental cornerstone in Autonomic Networking. It can be leveraged to secure all communications and thus allows automatic security without traditional configuration, for example, preshared keys. See also the document "Making The Internet Secure By Default" [Behringer] for more information.
Autonomic functions must be able to adapt their behavior depending on the domain of the node they are interacting with.
The goal of Autonomic Networking is to minimize dependencies on central elements; therefore, decentralization and distribution are fundamental to the concept. If a problem can be solved in a distributed manner, it should not be centralized.
In certain cases, it is today operationally preferable to keep a central repository of information, for example, a user database on an Authentication, Authorization, and Accounting (AAA) server. An Autonomic Network should be able to use such central systems, in order to be deployable. It is possible to distribute such databases as well, and such efforts should be at least considered. Depending on the case, distribution may not be simple replication but may involve more complex interactions and organization.
3.5. Simplification of Autonomic Node Northbound Interfaces
Even in a decentralized solution, certain information flows with central entities are required. Examples are high-level service definitions, as well as network status requests, audit information, logging, and aggregated reporting.
Therefore, nodes in an Autonomic Network require a northbound interface. However, the design goal is to maintain this interface as simple and high level as possible.
An administrator or autonomic management system interacts with an Autonomic Network on a high level of abstraction. Intent is defined at a level of abstraction that is much higher than that of typical configuration parameters, for example, "optimize my network for energy efficiency". Intent must not be used to convey low-level commands or concepts, since those are on a different abstraction level.
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For example, the administrator should not be exposed to the version of the IP protocol running in the network.
Also on the reporting and feedback side, an Autonomic Network abstracts information and provides high-level messages such as "the link between node x and y is down" (possibly with an identifier for the specific link, in case of multiple links).
An Autonomic Network, while minimizing the need for user intervention, still needs to provide users with visibility like in traditional networks. However, in an Autonomic Network, reporting should happen on a network-wide basis. Information about the network should be collected and aggregated by the network itself and presented in a consolidated fashion to the administrator.
The layers of abstraction that are provided via Intent need to be supported for reporting functions as well, in order to give users an indication about the effectiveness of their Intent. For example, in order to assess how effective the network performs with regards to the Intent "optimize my network for energy efficiency", the network should provide aggregate information about the number of ports that were able to be shut down, and the corresponding energy savings, while validating current service levels are, on aggregate, still met.
Autonomic network events should concern the Autonomic Network as a whole, not individual systems in isolation. For example, the same failure symptom should not be reported from every system that observes it, but only once for the Autonomic Network as a whole. Ultimately, the Autonomic Network should support exception-based management, in which only events that truly require user attention actually cause the user to be notified. This requires capabilities that allow systems within the network to compare information and apply specific algorithms to determine what should be reported.
[RFC7576] points out that there are already a number of autonomic functions available today. However, they are largely independent, and each has its own methods and protocols to communicate, discover, define, and distribute policy, etc.
The goal of the work on Autonomic Networking in the IETF is therefore not just to create autonomic functions but to define a common infrastructure that autonomic functions can use. This Autonomic Networking Infrastructure may contain common control and management
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functions such as messaging, service discovery, negotiation, Intent distribution, self-monitoring, and diagnostics, etc. A common approach to define and manage Intent is also required.
Refer to the reference model below: All the components around the "Autonomic Service Agents" should be common components, such that the Autonomic Service Agents do not have to replicate common tasks individually.
Autonomic functions may reside on any layer in the networking stack. For example, Layer 2 switching today is already relatively autonomic in many environments, since most switches can be plugged together in many ways and will automatically build a simple Layer 2 topology. Routing functions run on a higher layer and can be autonomic on Layer 3. Even application-layer functionality such as unified communications can be autonomic.
"Autonomic" in the context of this framework is a property of a function that is implemented on a node. Autonomic functions can be implemented on any node type, for example, a switch, router, server, or call manager.
An Autonomic Network requires an overall control plane for autonomic nodes to communicate. As in general IP networking, IP is the spanning layer that binds all those elements together; autonomic functions in the context of this framework should therefore operate at the IP layer. This concerns neighbor discovery protocols and other functions in the Autonomic Control Plane.
An autonomic function does not depend on external input to operate; it needs to understand its current situation and surroundings and operate according to its current state. Therefore, an autonomic function must understand the full life cycle of the device it runs on, from manufacturing and initial testing through deployment, testing, troubleshooting, and decommissioning.
The state of the life cycle of an autonomic node is reflected in a state model. The behavior of an autonomic function may be different for different deployment states.
This section identifies various items that are explicitly not design goals in the IETF and IRTF for Autonomic Networks; they are mentioned to avoid misunderstandings of the general intention. They address some commonly expressed concerns from network administrators and architects.
Section 3.1 states that "It is the design goal to make functions [...] minimally dependent on [...] human operators". However, it is not a design goal to completely eliminate them. The problem targeted by Autonomic Networking is the error-prone and hard-to-scale model of individual configuration of network elements, traditionally by manual commands but today mainly by scripting and/or configuration management databases. This does not, however, imply the elimination of skilled human operators, who will still be needed for oversight, policy management, diagnosis, reaction to help-desk tickets, etc. The main impact on administrators should be less tedious detailed work and more high-level work. (They should become more like doctors than hospital orderlies.)
However good the autonomous mechanisms, sometimes there will be fault conditions, etc., that they cannot deal with correctly. At that point, skilled operator interventions will be needed to correct or work around the problem. Hopefully, this can be done by high-level mechanisms (adapting the policy database in some way), but, in some cases, direct intervention at the device level may be unavoidable. This is obviously the case for hardware failures, even if the Autonomic Network has bypassed the fault for the time being. "Truck rolls" will not be eliminated when faulty equipment needs to be replaced. However, this may be less urgent if the autonomic system automatically reconfigures to minimize the operational impact.
While it is a goal to simplify northbound interfaces (Section 3.5), it is not a goal to eliminate central control, but to allow it on a higher abstraction level. Senior management might fear loss of control of an Autonomic Network. In fact, this is no more likely than with a traditional network; the emphasis on automatically applying general policy and security rules might even provide more central control.
An Autonomic Network consists of Autonomic Nodes. Those nodes communicate with each other through an Autonomic Control Plane that provides a robust and secure communications overlay. The Autonomic Control Plane is self-organizing and autonomic itself.
An Autonomic Node contains various elements, such as autonomic service agents that implement autonomic functions. Figure 1 shows a reference model of an autonomic node. The elements and their interaction are:
o Autonomic Service Agents: They implement the autonomic behavior of a specific service or function.
o Self-knowledge: An autonomic node knows its own properties and capabilities
o Network Knowledge (Discovery): An Autonomic Service Agent may require various discovery functions in the network, such as service discovery.
o Feedback Loops: Control elements outside the node may interact with autonomic nodes through feedback loops.
o An Autonomic User Agent, providing a front-end to external users (administrators and management applications) through which they can receive reports and monitor the Autonomic Network.
o Autonomic Control Plane: Allows the node to communicate with other autonomic nodes. Autonomic functions such as Intent distribution, feedback loops, discovery mechanisms, etc., use the Autonomic Control Plane. The Autonomic Control Plane can run in-band, over a configured VPN, over a self-managing overlay network as described in [ACP], or over a traditional out-of-band network. Security is a requirement for the Autonomic Control Plane, which can be bootstrapped by a mechanism as described in [BOOTSTRAP].
This document provides definitions and design goals for Autonomic Networking. A full threat analysis will be required as part of the development of solutions, taking account of potential attacks from within the network as well as from outside.
[ACP] Behringer, M., Bjarnason, S., BL, B., and T. Eckert, "An Autonomic Control Plane", Work in Progress, draft-behringer-anima-autonomic-control-plane-02, March 2015.
[Behringer] Behringer, M., Pritikin, M., and S. Bjarnason, "Making The Internet Secure By Default", Work in Progress, draft-behringer-default-secure-00, January 2014.
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[BOOTSTRAP] Pritikin, M., Behringer, M., and S. Bjarnason, "Bootstrapping Key Infrastructures", Work in Progress, draft-pritikin-anima-bootstrapping-keyinfra-01, February 2015.
[Dobson] Dobson, S., Denazis, S., Fernandez, A., Gaiti, D., Gelenbe, E., Massacci, F., Nixon, P., Saffre, F., Schmidt, N., and F. Zambonelli, "A survey of autonomic communications", ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 1, Issue 2, Pages 223-259, DOI 10.1145/1186778.1186782, December 2006.
[GANA] ETSI, "Autonomic network engineering for the self-managing Future Internet (AFI); Generic Autonomic Network Architecture (An Architectural Reference Model for Autonomic Networking, Cognitive Networking and Self- Management)", ETSI GS AFI 002, April 2013, <http://www.etsi.org/deliver/etsi_gs/ AFI/001_099/002/01.01.01_60/gs_afi002v010101p.pdf>.
[Kephart] Kephart, J. and D. Chess, "The Vision of Autonomic Computing", IEEE Computer, vol. 36, no. 1, pp. 41-50, DOI 10.1109/MC.2003.1160055, January 2003.
[Movahedi] Movahedi, Z., Ayari, M., Langar, R., and G. Pujolle, "A Survey of Autonomic Network Architectures and Evaluation Criteria", IEEE Communications Surveys & Tutorials, Volume 14, Issue 2, Pages 464-490, DOI 10.1109/SURV.2011.042711.00078, 2012.
[Reference-Model] Behringer, M., Ed., Carpenter, B., Eckert, T., Ciavaglia, L., and B. Liu, "A Reference Model for Autonomic Networking", Work in Progress, draft-behringer-anima- reference-model-02, June 2015.
[Samaan] Samaan, N. and A. Karmouch, "Towards Autonomic Network Management: an Analysis of Current and Future Research Directions", IEEE Communications Surveys and Tutorials, Volume 11, Issue 3, Page(s) 22-36, DOI 10.1109/SURV.2009.090303, 2009.
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Acknowledgements
Many parts of this work on Autonomic Networking are the result of a large team project at Cisco Systems. In alphabetical order: Ignas Bagdonas, Parag Bhide, Balaji BL, Toerless Eckert, Yves Hertoghs, Bruno Klauser.
We thank the following people for their input to this document: Dimitri Papadimitriou, Rene Struik, Kostas Pentikousis, Dave Oran, and Diego Lopez Garcia.
The ETSI working group AFI <http://portal.etsi.org/afi> defines a similar framework for Autonomic Networking in the "General Autonomic Network Architecture" [GANA]. Many concepts explained in this document can be mapped to the GANA framework. The mapping is outside the scope of this document. Special thanks to Ranganai Chaparadza for his comments and help on this document.
Authors' Addresses
Michael H. Behringer Cisco Systems Building D, 45 Allee des Ormes Mougins 06250 France
EMail: mbehring@cisco.com
Max Pritikin Cisco Systems 5330 Airport Blvd Boulder, CO 80301 United States
EMail: pritikin@cisco.com
Steinthor Bjarnason Cisco Systems Mail Stop LYS01/5 Philip Pedersens vei 1 LYSAKER, AKERSHUS 1366 Norway
EMail: sbjarnas@cisco.com
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Alexander Clemm Cisco Systems 170 West Tasman Drive San Jose, CA 95134-1706 United States
EMail: alex@cisco.com
Brian Carpenter Department of Computer Science University of Auckland PB 92019 Auckland 1142 New Zealand