{"title":"具有细粒度带宽控制的基于模型的应用程序自治管理器","authors":"Nasim Beigi Mohammadi, Mark Shtern, Marin Litoiu","doi":"10.23919/CNSM.2017.8255994","DOIUrl":null,"url":null,"abstract":"In this paper, we propose and implement a machine learning based application autonomic management system that controls the bandwidth rates allocated to each scenario of a web application to postpone scaling out for as long as possible. Through experiments on Amazon AWS cloud, we demonstrate that the autonomic manager is able to quickly meet Service level Agreement (SLA) and reduce the SLA violations by 56% compared to a previous heuristic-based approach.","PeriodicalId":211611,"journal":{"name":"2017 13th International Conference on Network and Service Management (CNSM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"A model-based application autonomic manager with fine granular bandwidth control\",\"authors\":\"Nasim Beigi Mohammadi, Mark Shtern, Marin Litoiu\",\"doi\":\"10.23919/CNSM.2017.8255994\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, we propose and implement a machine learning based application autonomic management system that controls the bandwidth rates allocated to each scenario of a web application to postpone scaling out for as long as possible. Through experiments on Amazon AWS cloud, we demonstrate that the autonomic manager is able to quickly meet Service level Agreement (SLA) and reduce the SLA violations by 56% compared to a previous heuristic-based approach.\",\"PeriodicalId\":211611,\"journal\":{\"name\":\"2017 13th International Conference on Network and Service Management (CNSM)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 13th International Conference on Network and Service Management (CNSM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.23919/CNSM.2017.8255994\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 13th International Conference on Network and Service Management (CNSM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.23919/CNSM.2017.8255994","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A model-based application autonomic manager with fine granular bandwidth control
In this paper, we propose and implement a machine learning based application autonomic management system that controls the bandwidth rates allocated to each scenario of a web application to postpone scaling out for as long as possible. Through experiments on Amazon AWS cloud, we demonstrate that the autonomic manager is able to quickly meet Service level Agreement (SLA) and reduce the SLA violations by 56% compared to a previous heuristic-based approach.
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