Self-Driving Networks

Self-Driving Networks

Kireeti Kompella (Juniper Networks, USA)
Copyright: © 2019 |Pages: 24
DOI: 10.4018/978-1-5225-7146-9.ch002
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This chapter presents a new vision of network operations, the self-driving network, that takes automation to the next level. This is not a description of existing work; rather, it is a challenge to dramatically rethink how we manage networks (or rather, how we do not manage networks). It draws upon an analogy with the development of self-driving cars and presents motivations for this effort. It then describes the technologies needed to implement this and an overall architecture of the system. As this endeavor will cause a major shift in network management, the chapter offers an evolutionary path to the end goal. Some of the consequences and human impacts of such a system are touched upon. The chapter concludes with some research topics and a final message. Key takeaways are that machine learning and feedback loops are fundamental to the solution; a key outcome is to build systems that are adaptive and predictive, for the benefit of users.
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Advances in autonomous systems1 are all around us. As processors become more powerful and more energy efficient; as data becomes more ubiquitous and purposeful; and as software gets more sophisticated; our ability to hand over control to machines increases. In a very real sense, this is the logical conclusion of automation, the progression being: do things manually; find (sub-)tasks that are repetitive and automate them; and eventually, give the whole job over to a machine. This process requires considerable technological progress, as well as human considerations. Both of these will be explored in this chapter. A useful intermediate stage between automation and full autonomy is “augmentation,” where man and machine cooperatively operate a system; this will be discussed as well.

The very visible face of autonomous systems today is the self-driving car. Here again, we have gone from a very manual approach, where humans control every aspect of driving, to automating various driving functions; and from this, to creating fully autonomous vehicles. Efforts to automate driving functions have been sprinkled throughout the car’s 130-year history; efforts to build self-driving cars are much more recent. A significant trigger in the latter came from the Defense Advanced Research Projects Agency’s Grand Challenge to build an autonomous ground vehicle, held in 2004. Fifteen teams participated, albeit unsuccessfully; the following year, though, 5 teams were successful. This journey since has been long, but finally appears quite near technological success; now, human considerations dominate. The author will draw on this analogy in the discussion of self-driving networks; there are valuable lessons to learn in so doing, while bearing in mind that no analogy is perfect.

Key Terms in this Chapter

Proactive Management: Action in anticipation of an event or condition that may affect a system, based on a prediction that the event will occur, or the condition will arise.

Prediction: The act of taking information one has and generating information one didn’t previously have.

Augmentation: A system where man and machine work together, leveraging each partner’s strengths and compensating for the other’s weaknesses, to achieve a certain goal.

Autonomous System: A system in charge of some entity (such as a network) that monitors conditions, evaluates them in the light of some goal, and makes decisions to achieve the goal, without the help of a human.

Intent: An abstract, declarative statement of a desired state or action.

Service Motion: The live migration of a network service from a port on a network device to a different port on the same or a different network device.

Machine Learning: A field of computer science that uses statistical techniques to give computer systems the ability to “learn” (e.g., progressively improve performance on a specific task) with data, without being explicitly programmed.

Telemetry: the measurement and transmission of the readings of a sensor to a collector where the information is stored and catalogued.

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