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

CSR：小型：云中的 PROM：利用云中的性能优化调度

• 批准号: 1320478
• 负责人: Chitaranjan Das
• 金额: $ 49.52万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320478&HistoricalAwards=false
• 关键词: CSR; Small; PROM; Clouds; Exploiting

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.

云计算正在成为一种有吸引力的计算范式，其中硬件和软件资源都可以从云提供商那里租用，从而为大量服务实现更好的规模经济、弹性、可用性和灵活性。设计此类云平台的一个主要挑战是提供高且可预测的性能以及服务质量（QoS）保证。性能的不可预测性归因于许多因素，包括系统规模、工作负载动态、虚拟化开销、资源共享和争用。本提案的目标是通过三个相互交织的任务来研究云环境中的性能增强和性能量化技术。首先，将开发一个由高级作业调度器和细粒度资源管理器组成的综合调度框架，以有效地管理云资源以增强性能。其次，将开发用于估计云平台中作业完成时间（JCT）的性能预测模型。第三，为了提供QoS保证，将开发一个控制理论模型，该模型结合了所提出的调度程序和分析模型。使用MapReduce和其他代表性云工作负载在真实平台上的测量将用于验证所提出的想法。这项研究解决了云计算的一个主要问题——如果我的应用程序迁移到云端，对性能的影响是什么？如果成功地回答了这个问题，它将对启用云的应用程序域产生巨大的影响。这项工作的交叉性质可以培育新的研究途径。除了在建筑、分布式系统和性能建模等领域对本科生和研究生进行培训外，该项目还将特别关注女性和少数族裔学生的参与。本研究开发的建模工具和技术将向公众开放。