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

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个重要的服务保证：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，同时不影响其他目标，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和中间件方法中的干扰和硬件异构问题，从而执行敏捷、非侵入性和以应用程序为中心的资源供应。该研究方法将并行多线程、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，并集成自优化学习和控制技术的力量，以实现动态工作负载下的自动化资源供应。该项目通过探索计算机系统设计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中的资源管理和计量。该项目还涉及行业合作、课程开发，并为女性、少数民族和代表性不足的学生进入研究和研究生项目提供更多途径。

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.