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现有云提供商缺少两项重要的服务保证：资源容量和应用程序敏捷性。为了提供有保证的资源容量和增强的应用程序敏捷性，该项目在系统和中间件级别开发了独立但互补的方法，以减少云内应用程序的性能变化，而不影响其他目标，例如高数据中心利用率和良好的平均性能。预期的交付成果包括云资源管理中的新系统支持，以解决共享基础设施中的干扰和硬件异构性，以及执行敏捷、非侵入性和以应用程序为中心的资源配置的中间件方法。 该研究方法将同步多线程、多核和非均匀内存访问架构之间复杂相互作用的架构知识与统计学习算法相结合，以量化干扰和异构性，并集成自优化学习和控制技术的优势，以实现资源自动化动态工作负载下的配置。 该项目通过探索计算机系统设计中的跨学科技术并通过可预测性保证增强云服务来扩大影响。 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，并提供更多途径让女性、少数族裔和代表性不足的学生参与研究和研究生项目。