CSR: Small: System and Middleware Approaches to Predictable Services in Multi-Tenant Clouds

CSR：小：多租户云中可预测服务的系统和中间件方法

• 批准号: 1649502
• 负责人: Jia Rao
• 金额: $ 13.47万
• 依托单位: University of Texas at Arlington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649502&HistoricalAwards=false
• 关键词: CSR; Small; System; Middleware; Approaches

Datacenter-based cloud services exhibit unpredictable performance variations due to multi-tenant interferences and the heterogeneity in datacenter hardware. The investigators attribute the causes of such performance unpredictability to missing two important service guarantees from existing cloud providers: resource capacity and application agility. To provide guaranteed resource capacity and enhanced application agility, this project develops independent but complementary approaches at system and middleware levels to reduce performance variations of in-cloud applications without compromising other objectives such as high datacenter utilization and good average performance. Anticipated deliverables include new system support in cloud resource management to account for interferences and hardware heterogeneity in shared infrastructures and middleware approaches to perform agile, non-invasive and application-centric resource provisioning. The research methodology combines architectural knowledge on the complex interplay between simultaneous multi-threading, multicore, and non-uniform memory access architectures with statistical learning algorithms to quantify interference and heterogeneity, and integrates the strength of self-optimizing learning and control techniques to automate resource provisioning under dynamic workloads. This project broadens impact by exploring inter-disciplinary techniques in computer system design and enhancing cloud services with predictability guarantees. The success will guide resource management and metering in future cloud systems. This project also involves industry collaboration, curriculum development, and provides more avenues to bring women, minority, and underrepresented students into research and graduate programs.

由于多租户干扰和数据中心硬件的异构性，基于数据中心的云服务表现出不可预测的性能变化。调查人员将这种性能不可预测性的原因归因于缺乏现有云提供商的两个重要服务保证：资源容量和应用灵活性。为了提供有保证的资源容量和增强的应用灵活性，该项目在系统和中间件级别开发独立但互补的方法，以减少云中应用程序的性能差异，而不会影响其他目标，如高数据中心利用率和良好的平均性能。预期交付成果包括云资源管理中的新系统支持，以应对共享基础设施中的干扰和硬件异构性，以及执行灵活、非侵入性和以应用为中心的资源调配的中间件方法。该研究方法将关于同时多线程、多核和非统一内存访问体系结构之间复杂相互作用的体系结构知识与统计学习算法相结合，以量化干扰和异构性，并整合自我优化学习和控制技术的优势，以在动态工作负载下自动执行资源调配。该项目通过探索计算机系统设计中的跨学科技术和增强具有可预测性保证的云服务来扩大影响。这一成功将指导未来云系统的资源管理和计量。该项目还涉及行业合作、课程开发，并提供了更多途径，将女性、少数族裔和代表性不足的学生带入研究和研究生课程。