{"title":"Automated and dynamic application accuracy management and resource provisioning in a cloud environment","authors":"S. Vijayakumar, Qian Zhu, G. Agrawal","doi":"10.1109/GRID.2010.5697963","DOIUrl":null,"url":null,"abstract":"The recent emergence of cloud computing is making the vision of utility computing realizable, i.e., computing resources and services from a cloud can be delivered, utilized, and paid for in the same fashion as utilities like water or electricity. This, however, creates new resource provisioning problems. Because of the pay-as-you-go model, resource provisioning should be performed carefully. Resource provisioning can be particularly challenging for adaptive applications, where there can be a tradeoff between the application Quality of Service (QoS), or accuracy, and the resource costs incurred. In this paper, we consider adaptive streaming applications where a user wants to achieve the minimum resource costs while maintaining a specified accuracy goal. We present a dynamic and automated framework which can adapt the adaptive parameters to meet the specific accuracy goal, and then dynamically converge to near-optimal resource allocation. Our solution can handle unexpected changes in the data distribution characteristics and/or rates. We evaluate our approach using two streaming applications and demonstrate the effectiveness of our framework.","PeriodicalId":6372,"journal":{"name":"2010 11th IEEE/ACM International Conference on Grid Computing","volume":"72 1","pages":"33-40"},"PeriodicalIF":0.0000,"publicationDate":"2010-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 11th IEEE/ACM International Conference on Grid Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/GRID.2010.5697963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10
Abstract
The recent emergence of cloud computing is making the vision of utility computing realizable, i.e., computing resources and services from a cloud can be delivered, utilized, and paid for in the same fashion as utilities like water or electricity. This, however, creates new resource provisioning problems. Because of the pay-as-you-go model, resource provisioning should be performed carefully. Resource provisioning can be particularly challenging for adaptive applications, where there can be a tradeoff between the application Quality of Service (QoS), or accuracy, and the resource costs incurred. In this paper, we consider adaptive streaming applications where a user wants to achieve the minimum resource costs while maintaining a specified accuracy goal. We present a dynamic and automated framework which can adapt the adaptive parameters to meet the specific accuracy goal, and then dynamically converge to near-optimal resource allocation. Our solution can handle unexpected changes in the data distribution characteristics and/or rates. We evaluate our approach using two streaming applications and demonstrate the effectiveness of our framework.