Virtual Machine Management for Dynamic Vehicular Clouds

Abstract

Vehicular clouds involve a dynamic environment where virtual machines are hosted on moving vehicles, leading to frequent changes in the data center network topology. These frequent topological changes require frequent virtual machine migrations in order to meet the service level agreements with cloud users. Such topology changes include fluctuations in connectivity, signal strength and quality. Few studies address vehicles as potential virtual machine hosts, while there is a significant opportunity in capitalizing on underutilized resources. Due to the rapidly changing environment of a vehicular cloud, hosts frequently change or leave coverage. As such, virtual machine management and migration schemes are necessary to ensure cloud subscribers have a satisfactory level of access to the resources. This thesis addresses the need for virtual machine management for the vehicular cloud. First, a mobility model is proposed and utilized to test a set of novel Vehicular Virtual Machine Migration (VVMM) schemes: VVMM-U (Uniform), VVMM-LW (Least Workload), VVMM-MA (Mobility Aware) and MDWLAM (Mobility and Destination Workload Aware Migration). Their performance is evaluated with respect to a set of metrics through simulations with varying levels of vehicular traffic congestion, virtual machine sizes and load restriction levels. The most advanced scheme (MDWLAM) takes into account the workload and mobility of the original host as well as those of the potential destinations. By doing so a valid destination will both have time to receive the workload and migrate the new load when necessary. The behavior of various algorithms is compared and the MDWLAM has been shown to demonstrate the best performance, exhibiting migration drop rates that are negligibly small. Finally, an integer linear program formulation based on a modified single source shortest path problem is presented, tested and successfully shown to be a benchmark that can be used in comparison to the proposed heuristics.