{"title":"Self-Driving Networks","authors":"K. Kompella","doi":"10.4018/978-1-5225-7146-9.CH002","DOIUrl":null,"url":null,"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.","PeriodicalId":130517,"journal":{"name":"Research Anthology on Cross-Disciplinary Designs and Applications of Automation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research Anthology on Cross-Disciplinary Designs and Applications of Automation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4018/978-1-5225-7146-9.CH002","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
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.