A ga-based approach for resource consolidation of virtual machines in clouds

In cloud computing, infrastructure as a service (IaaS) is a growing market that enables users to access cloud resources in the convenient, on-demand manner. The IaaS can provide user to rent the resources of cloud computing and virtual machines (VMs) through virtualization technology. Because different VMs may demand different amounts of resources, an important problem that must be addressed effectively in the cloud is how to decide the mapping adaptively in order to satisfy the resource needs of VMs. The mapping problem solution is called virtual machine placement policy (VMPP). However, VM will change the requirement of resources according to the workload of application VM. Thus, it's necessary to apply resource consolidation technology to satisfy dynamically resource on demand. In this thesis, we present a two-phase approach for resource consolidation to minimize resource consumption. In the first phase, we use a genetic algorithm to find a reconfiguration plan. In the second phase, we propose a mechanism to find a way to migrate VMs such that the number of active nodes and the overall migration cost could be minimized. Finally, the experimental results show that we obtain well-consolidating active nodes than other existing approaches.