CSR: Small: PROM in Clouds: Exploiting Scheduling for PeRformance OptiMization in Clouds

点击翻译按钮获取中文摘要

Cloud computing is emerging as an attractive computing paradigm, whereboth hardware and software resources can be leased from a cloudprovider to achieve better economy of scale, elasticity, availability,and flexibility for a plethora of services. A major challenge indesigning such cloud platforms is in providing high and predictableperformance along with Quality of Service (QoS) guarantees. Theperformance unpredictability is attributed to a myriad of factorsincluding system scale, workload dynamism, virtualization overheads,resource sharing and contention.The objective of this proposal is to investigate performanceenhancement and performance quantification techniques in a cloudenvironment through three intertwined tasks.First, a comprehensive scheduling framework consisting of a high-leveljob scheduler and a fine-grained resource manager will be developedfor efficient management of cloud resources for performance enhancement.Second, performance prediction models will be developed for estimatingjob completion time (JCT) in a cloud platform.Third, for providing QoS guarantees, a control-theoretic modelincorporating the proposed scheduler and analytical models will bedeveloped. Measurements on real platforms with MapReduce and other representative cloud workloads will be used to validate theproposed ideas.This research addresses one of the main concerns of cloud computing -What are the performance implications if my application is moved to acloud? If successful in answering this question, it would have a tremendousimpact on the cloud-enabled application domains. The cross-cuttingnature of this work can foster new research avenues.In addition to undergraduate and graduate student training in areassuch as architecture, distributed systems, and performance modeling,special attention will be given to involving women and minoritystudents in this project. The modeling tools and techniques developedin this research will be made publicly available.