Workload interleaving with performance guarantees in data centers

In the era of global, large scale data centers residing in clouds, many applications and users share the same pool of resources for the purpose of reducing costs while maintaining high performance. When multiple workloads access the same resources concurrently, their requests are interleaved, possibly causing delays. Providing performance isolation to individual workloads such that they meet their own performance objectives is important and challenging. The challenge lies in finding accurate, robust, compact metrics and models that drive algorithms which can meet different performance objectives while achieving efficient utilization of resources. This dissertation proposes a set of methodologies and tools aiming at solving the challenging performance isolation problem of workload interleaving in data centers, focusing on both storage components and computing components. At the storage node level, we consider methodologies for better interleaving user traffic with background workloads, such as tasks for improving reliability, availability, and power savings. At the storage cluster level, we propose methodologies on how to efficiently conduct work consolidation and schedule asynchronous updates without violating user performance targets. At the computing node level, we present priority scheduling middleware that employs different policies to schedule background tasks. Finally, at the computing cluster level, we develop a new Hadoop scheduler called DyScale to exploit capabilities offered by heterogeneous cores in order to achieve a variety of performance objectives. All works have been evaluated through extensive simulation using enterprise traces or real testbed implementation, and have been accepted for publications in leading performance conferences.