{"title":"Research on Cloud Resource Optimization Model Based on Users' Satisfaction","authors":"Z. Ao, Ming-hai Jiao, Ke Gao, Xingwei Wang","doi":"10.1109/WISA.2016.29","DOIUrl":"https://doi.org/10.1109/WISA.2016.29","url":null,"abstract":"An improved cloud resource revenue optimization models are proposed, which include the benefits of service providers and users negotiate SLA agreement. And the models define the constraint mechanism for describing service quality problems such as service response time and customer satisfaction. We design the comprehensive response indicator and use this indicator to quantify the rationality of the distribution of resources, which are the equilibrium between maximal revenue of cloud service providers and the quality of service for users. The parallel PSO-ACO algorithm based on MapReduce for distributed systems is presented to solve the above model, which represents the massive resource allocation optimization problem. The experiments are implemented to analyze the algorithm performance and task allocation strategy. And the experiments results show that the models and algorithms are robust and efficient.","PeriodicalId":308797,"journal":{"name":"2016 13th Web Information Systems and Applications Conference (WISA)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121594914","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}