{"title":"Wireless Optimisation via Convex Bandits: Unlicensed LTE/WiFi Coexistence","authors":"C. Cano, Gergely Neu","doi":"10.1145/3229543.3229551","DOIUrl":"https://doi.org/10.1145/3229543.3229551","url":null,"abstract":"Bandit Convex Optimisation (BCO) is a powerful framework for sequential decision-making in non-stationary and partially observable environments. In a BCO problem, a decision-maker sequentially picks actions to minimize the cumulative cost associated with these decisions, all while receiving partial feedback about the state of the environment. This formulation is a very natural fit for wireless-network optimisation problems and has great application potential since: i) instead of assuming full observability of the network state, it only requires the metric to optimise as input, and ii) it provides strong performance guarantees while making only minimal assumptions about the network dynamics. Despite these advantages, BCO has not yet been explored in the context of wireless-network optimisation. In this paper, we make the first steps to demonstrate the potential of BCO techniques by formulating an unlicensed LTE/WiFi fair coexistence use case in the framework, and providing empirical results in a simulated environment. On the algorithmic front, we propose a simple and natural sequential multi-point BCO algorithm amenable to wireless networking optimisation, and provide its theoretical analysis. We expect the contributions of this paper to pave the way to further research on the application of online convex methods in the bandit setting.","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124914425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Christopher Streiffer, Huan Chen, Theophilus A. Benson, Asim Kadav
{"title":"DeepConf: Automating Data Center Network Topologies Management with Machine Learning","authors":"Christopher Streiffer, Huan Chen, Theophilus A. Benson, Asim Kadav","doi":"10.1145/3229543.3229554","DOIUrl":"https://doi.org/10.1145/3229543.3229554","url":null,"abstract":"In recent years, many techniques have been developed to improve the performance and efficiency of data center networks. While these techniques provide high accuracy, they are often designed using heuristics that leverage domain-specific properties of the workload or hardware. In this vision paper, we argue that many data center networking techniques, e.g., routing, topology augmentation, energy savings, with diverse goals share design and architectural similarities. We present a framework for developing general intermediate representations of network topologies using deep learning that is amenable to solving a large class of data center problems. We develop a framework, DeepConf, that simplifies the process of configuring and training deep learning agents by using our intermediate representation to learn different tasks. To illustrate the strength of our approach, we implemented and evaluated a DeepConf-agent that tackles the data center topology augmentation problem. Our initial results are promising --- DeepConf performs comparably to the optimal solution.","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124037360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Proceedings of the 2018 Workshop on Network Meets AI & ML","authors":"","doi":"10.1145/3229543","DOIUrl":"https://doi.org/10.1145/3229543","url":null,"abstract":"","PeriodicalId":198478,"journal":{"name":"Proceedings of the 2018 Workshop on Network Meets AI & ML","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126915256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}