Modern virtual machine (VM) management software enables consolidation of VMs for power savings or load-balancing for performance. While existing literature provides various methods for computing a better load-balanced, or consolidated goal state, it fails to adequately suggest the best path from the system’s current state to the desired goal allocation. This paper discusses an approach to efficient path finding in VM placement problems for cloud computing environments of moderate scale with results indicating the solution is reasonable for managing hundreds of VMs. We present an overview of known approaches to dynamic VM placement and discuss their shortcomings with respect to dynamic reallocation. We then describe a novel design and implementation of a heuristic search algorithm to determine optimal sequential migration plans to transition from a given VM-to-host allocation to an arbitrary desired allocation state. We then elaborate nuances of A* application to this domain along with our simulation-based validation approach. Finally, this work demonstrates a novel and highly effective technique for exploiting migration parallelism in order to rapidly achieving VM reallocation convergence suitable for continual workload rebalancing in cloud data centers.
['Eli M. Dow', 'Jeanna Neefe Matthews']
WAYFINDER: parallel virtual machine reallocation through A* search