Energy-efficient management of virtual machines in data centers for cloud computing

Cloud computing has revolutionized the information technology industry by enabling elastic on-demand provisioning of computing resources. The proliferation of Cloud computing has resulted in the establishment of large-scale data centers around the world containing thousands of compute nodes. However, Cloud data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. In 2010, energy consumption by data centers worldwide was estimated to be between 1.1% and 1.5% of the global electricity use and is expected to grow further. This thesis presents novel techniques, models, algorithms, and software for distributed dynamic consolidation of Virtual Machines (VMs) in Cloud data centers. The goal is to improve the utilization of computing resources and reduce energy consumption under workload independent quality of service constraints. Dynamic VM consolidation leverages fine-grained fluctuations in the application workloads and continuously reallocates VMs using live migration to minimize the number of active physical nodes. Energy consumption is reduced by dynamically deactivating and reactivating physical nodes to meet the current resource demand. The proposed approach is distributed, scalable, and efficient in managing the energy-performance trade-off. The key contributions are: 1. Competitive analysis of dynamic VM consolidation algorithms and proofs of the competitive ratios of optimal online deterministic algorithms for the formulated single VM migration and dynamic VM consolidation problems. 2. A distributed approach to energy-efficient dynamic VM consolidation and several novel heuristics following the proposed approach, which lead to a significant reduction in energy consumption with a limited performance impact, as evaluated by a simulation study using real workload traces. 3. An optimal offline algorithm for the host overload detection problem, as well as a novel Markov chain model that allows a derivation of an optimal randomized control policy under an explicitly specified QoS goal for any known stationary workload and a given state configuration in the online setting. 4. A heuristically adapted host overload detection algorithm for handling unknown non-stationary workloads. The algorithm leads to approximately 88% of the mean inter-migration time produced by the optimal offline algorithm. 5. An open source implementation of a software framework for distributed dynamic VM consolidation called OpenStack Neat. The framework can be applied in both further research on dynamic VM consolidation, and real OpenStack Cloud deployments to improve the utilization of resources and reduce energy consumption.