Multi-objective virtual machine management in Cloud data centers

Abstract

Cloud Computing has recently emerged as a highly successful alternative information technology paradigm through on-demand resource provisioning and almost perfect reliability. In order to meet the customer demands, Cloud providers are deploying large-scale virtualized data centers consisting of thousands of servers across the world. These data centers require huge amount of electrical energy that incur very high operating cost and as a result, leave large carbon footprints. The reason behind the extremely high energy consumption is not just the amount of computing resources used, but also lies in inefficient use of these resources. Furthermore, with the recent proliferation of communication-intensive applications, network resource demands are becoming one of the key areas of performance bottleneck. As a consequence, efficient utilization of data center resources and minimization of energy consumption are emerging as critical factors for the success of Cloud Computing. This thesis addresses the above mentioned resource and energy related issues by tackling through data center-level resource management, in particular, by efficient Virtual Machine (VM) placement and consolidation strategies. The problem of high resource wastage and energy consumption is dealt with an online consolidated VM cluster placement scheme, utilizing the Ant Colony Optimization (ACO) metaheuristic and a vector algebra-based multi-dimensional resource utilization model. In addition, optimization of network resource utilization is addressed by an online network-aware VM cluster placement strategy in order to localize data traffic among communicating VMs and reduce traffic load in data center interconnects that, in turn, reduces communication overhead in the upper layer network switches. Besides the online placement schemes that optimize the VM placement during the initial VM deployment phase, an offline decentralized dynamic VM consolidation framework and an associated algorithm leveraging VM live migration technique are presented to further optimize the run-time resource usage and energy consumption, along with migration overhead minimization. Such migration-aware dynamic VM consolidation strategy uses realistic VM migration parameters to estimate impacts of necessary VM migrations on data center and hosted applications. Simulation-based performance evaluation using representative workloads demonstrates that the proposed VM placement and consolidation strategies are capable of outperforming the state-of-the-art techniques, in the context of large data centers, by reducing energy consumption up to 29%, server resource wastage up to 85%, and network load up to 60%.