Cloud computing has grown in prevalence from recent years due to its concept of computing as a service, thereby, allowing users to offload the infrastructure management costs and tasks to a cloud provider. Cloud providers leverage server virtualization technology for efficient resource utilization, faster provisioning times, reduced energy consumption, etc. Cloud computing inherits a key feature of server virtualization which is the live migration of virtual machines (VMs). This technique allows transferring of a VM from one host to another with minimal service interruption. However, live migration is a complex process and with a cloud management software used by cloud providers for management, there could be a significant influence on the migration process.

This thesis work aims to investigate the complex process of live migration performed by the hypervisor as well as the additional steps involved when a cloud management software or platform is present and form a timeline of these collection of steps or phases. The work also aims to investigate the performance of these phases, in terms of time, when migrating VMs with different sizes and workloads. For this thesis, the Kernel-based Virtual Machine (KVM) hypervisor and the OpenStack cloud software have been considered.

The methodology employed is experimental and quantitative. The essence of this work is investigation by network passive measurements. To elaborate, this thesis work performs migrations on physical test-beds and uses measurements to investigate and evaluate the migration process performed by the KVM hypervisor as well as the OpenStack platform deployed on KVM hypervisors. Experiments are designed and conducted based on the objectives to be met.

The results of the work primarily include the timeline of the migration phases of both the KVM hypervisor and the OpenStack platform. Results also include the time taken by each migration phase as well as the total migration time and the VM downtime. The results indicate that the total migration time, downtime and few of the phases increase with increase in CPU load and VM size. However, some of the phases do not portray any such trend. It has also been observed that the transfer stage alone does not contribute and influence the total time but every phase of the process has significant influence on the migration process.

The conclusions from this work is that although a cloud management software aids in managing the infrastructure, it has notable impact on the migration process carried out by the hypervisor. Moreover, the migration phases and their proportions not only depend on the VM but on the physical environment as well. This thesis work focuses solely on the time factor of each phase. Further evaluation of each phase with respect to its resource utilization can provide better insight into probable optimization opportunities.