A Robust Tabu Search Heuristic for VM Consolidation under Demand Uncertainty in Virtualized Datacenters

Abstract

In virtualized datacenters (vDCs), dynamic consolidation of virtual machines (VMs) is used as one of the most common techniques to achieve both energy-and resource-utilization efficiency. Live migrations of VMs are used for dynamic consolidation but due to dynamic resource demand variation of VMs may lead to frequent and non-optimal migrations. Assuming deterministic workload of the VMs may ensure the most energy/resource-efficient VM allocations but eventually may lead to significant resource contention or under-utilization if the workload varies significantly over time. On the other hand, adopting a conservative approach by allocating VMs depending on their peak demand may lead to low utilization, if the peak occurs infrequently or for a short period of time. Therefore, in this work we design a robust VM migration scheme that strikes a balance between protection for resource contention and additional energy costs due to powering on more servers while considering uncertainties on VMs resource demands. We use the theory of Gamma robustness and derive a robust Mixed Integer Linear programming (MILP) formulation. Due to the complexity, the problem is hard to solve for online optimization and we propose a novel heuristic based on Tabu Search. Using several scenarios, we show that that the proposed heuristic can achieve near optimal solution qualities in a short time and scales well with the instance sizes. Moreover, we quantitatively analyze the trade-off between energy cost versus protection level and robustness.