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

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

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.