Self-Driving Networks

Author:

Kompella Kireeti1

Affiliation:

1. Juniper Networks, USA

Abstract

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.

Publisher

IGI Global

Reference26 articles.

1. ANIMA WG. (2018, March). Retrieved April 2018, from IETF: https://datatracker.ietf.org/wg/anima/about/

2. Ansible. (2018, June 1). Retrieved from Ansible: https://www.ansible.com

3. Barker, I. (2018, February). Security service providers suffer from false positive alert overload. Retrieved April 2018, from Beta news: https://betanews.com/2018/02/12/false-positive-alert-overload/

4. Bilger, B. (2013, November 25). Auto Correct. The New Yorker.

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1. Intent-Based Network Management;Future Networks, Services and Management;2021

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