T. Cucinotta, Luigi Pannocchi, Filippo Galli, S. Fichera, Sourav Lahiri, Antonino Artale
{"title":"Optimum VM Placement for NFV Infrastructures","authors":"T. Cucinotta, Luigi Pannocchi, Filippo Galli, S. Fichera, Sourav Lahiri, Antonino Artale","doi":"10.1109/IC2E55432.2022.00029","DOIUrl":null,"url":null,"abstract":"This paper constitutes an industrial experience re-port about the use of data center optimization strategies for softwarized network services within the Vodafone resource man-agement unit for the management of virtualized network infras-tructures. The problem of optimum virtual machine placement as needed in the network operator context is detailed, and different solving strategies are proposed and discussed, including heuristics based on genetic optimization. Also, experimental results are presented that compare these strategies with one another from the standpoint of optimality and execution times, using a data-set made of some of the real problems that had to be solved in the past few years by Vodafone, in order to optimize its capacity planning decisions. The presented experimental results highlight that an optimum solver leads to excessively high computation times for large problems, whereas simple heuristics may exhibit significant loss in optimality at reduced computation times. Genetic optimization, on the other hand, constitutes a very interesting trade-off between these two extremes. The data-set used for the provided results is published under an open data license, for possible reuse in future research works on the topic.","PeriodicalId":415781,"journal":{"name":"2022 IEEE International Conference on Cloud Engineering (IC2E)","volume":"223 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE International Conference on Cloud Engineering (IC2E)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC2E55432.2022.00029","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 paper constitutes an industrial experience re-port about the use of data center optimization strategies for softwarized network services within the Vodafone resource man-agement unit for the management of virtualized network infras-tructures. The problem of optimum virtual machine placement as needed in the network operator context is detailed, and different solving strategies are proposed and discussed, including heuristics based on genetic optimization. Also, experimental results are presented that compare these strategies with one another from the standpoint of optimality and execution times, using a data-set made of some of the real problems that had to be solved in the past few years by Vodafone, in order to optimize its capacity planning decisions. The presented experimental results highlight that an optimum solver leads to excessively high computation times for large problems, whereas simple heuristics may exhibit significant loss in optimality at reduced computation times. Genetic optimization, on the other hand, constitutes a very interesting trade-off between these two extremes. The data-set used for the provided results is published under an open data license, for possible reuse in future research works on the topic.