Stochastic Virtual Machine Placement for Cloud Data Centers Under Resource Requirement Variations

Abstract

In cloud computing environment, the optimal placement of virtual machines (VMs) onto physical servers has been of great importance to improving the resource utilization and energy efficiency of data centers. In this work, we study the VM placement problem for minimizing the total energy consumption in a data center under the uncertainty of resource requirements demanded by the VMs. Instead of using deterministic values to represent the resource requirements, as in most existing placers, we propose a stochastic placement approach in which the resource requirement variations are modeled as random variables. We further formulate the uncertainty-aware VM placement problem as a stochastic optimization model, of which the optimization objective is to minimize the total energy consumed by all physical machines (PMs). In the presence of varying resource requirements, the optimization model is subject to a probabilistic constraint on resource overflow probability on each PM (i.e., the probability of demanded CPU/memory exceeding the maximum capacity the PM can provide). To solve this stochastic optimization problem, we develop an efficient metaheuristic to seek for an optimized VM placement solution that minimizes the total energy cost while satisfying the probabilistic resource constraint. Moreover, by incorporating a solution initialization procedure and a neighborhood search strategy, we can further improve the effectiveness of the metaheuristic in solution space exploration. Extensive simulations are performed to justify the proposed approach, in terms of both solution feasibility and energy efficiency. By taking into account the uncertainty of resource requirements, the stochastic method can achieve more energy-efficient placement solutions compared with the deterministic VM placement algorithm.