Autonomic performance and power control in virtualized datacenters

Virtualized datacenters, the platform for supporting Cloud computing, allow diverse applications to share the underlying server resources. Due to the highly dynamic nature of Internet workloads, increasing complexity of applications, and complex dynamics of shared infrastructure, datacenters face significant challenges in managing application performance while maintaining resource utilization efficiency and reducing power consumption costs. This thesis presents middleware approaches to autonomic performance and power control in virtualized datacenters. To this end, we designed self-adaptive resource management techniques based on queuing models, machine learning and feedback control theory.;Firstly, we designed an efficient server provisioning mechanism based on end-to-end resource allocation optimization for client perceived response time guarantee in a multi-tier server cluster. To guarantee an important percentile-based performance in the face of highly dynamic workloads, we developed a self-adaptive and model-independent neural fuzzy controller, which is capable of self-constructing and adapting its server allocation policies.;Secondly, we developed a non-invasive and energy-efficient mechanism for performance isolation of co-located applications on virtualized servers. Thirdly, we designed a system that provides coordinated power and performance control in a virtualized server cluster through a Fuzzy MIMO controller. We further developed a distributed and interference-aware control framework for large complex systems.;Furthermore, we developed a power-aware framework based on GPU virtualization for managing scientific workloads running in GPU clusters. It improves the system energy efficiency through dynamic consolidation and placement of GPU workloads.;Finally, we developed an automation tool for joint resource allocation and configuration of Hadoop MapReduce framework, for cost-efficient Big Data Processing in the Cloud. It addresses the significant challenge of provisioning ad-hoc jobs that have performance deadlines through a novel two-phase machine learning and optimization framework.;We implemented and evaluated the proposed techniques in a testbed of virtualized blade servers hosting multi-tier applications, SPEC CPU2006 benchmarks, and Hadoop microbenchmarks. For evaluating power management of GPU clusters, we used NVIDIA Tesla C1060 GPUs. This thesis provides novel resource management solutions that control the quality of service provided by virtualized resources, improve the energy efficiency of the underlying system, and reduce the burden of complex system management from human operators.