Jun-Na Zhang, Shangguang Wang, Qibo Sun, Fangchun Yang
{"title":"Tradeoff between executing time and revenue for runtime service composition","authors":"Jun-Na Zhang, Shangguang Wang, Qibo Sun, Fangchun Yang","doi":"10.1109/IWQoS.2016.7590408","DOIUrl":null,"url":null,"abstract":"Given a service composition, it is challenging but important to have a runtime adaptation, due to the complicated execution environment and evolving feature of Web service. In this paper, we present a runtime adaptive service composition approach, taking execution time minimization and revenue maximization into consideration. Based on dynamic programming, we deduce the optimal policy. Through this policy, orchestrator selects one concrete service for per task on runtime. The experimental results show that the proposed approach outperforms previous approach.","PeriodicalId":304978,"journal":{"name":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWQoS.2016.7590408","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Given a service composition, it is challenging but important to have a runtime adaptation, due to the complicated execution environment and evolving feature of Web service. In this paper, we present a runtime adaptive service composition approach, taking execution time minimization and revenue maximization into consideration. Based on dynamic programming, we deduce the optimal policy. Through this policy, orchestrator selects one concrete service for per task on runtime. The experimental results show that the proposed approach outperforms previous approach.