Cost-Aware VM Placement Across Distributed DCs Using Bayesian Networks

In recent years, cloud computing providers have been working to provide highly available and scalable cloud services to keep themselves alive in the competitive market of various cloud services. The difficulty is that to provide such high quality services, they need to enlarge data centers (DCs), and consequently, to increase operating costs. Hence, leveraging cost-aware solutions to manage resources is necessary for cloud providers to decrease the total energy consumption, while keeping their customers satisfied with high quality services. In this paper, we consider the cost-aware virtual machine (VM) placement across geographically distributed DCs as a multi-criteria decision making problem and propose a novel approach to solve it by utilizing Bayesian Networks and two algorithms for VM allocation and consolidation. The novelty of our work lays in building the Bayesian Network according to the extracted expert knowledge and the probabilistic dependencies among parameters to make decisions regarding cost-aware VM placement across distributed DCs, which can face power outages. Moreover, to evaluate the proposed approach we design a novel simulation framework that provides the required features for simulating distributed DCs. The performance evaluation results reveal that using the proposed approach can reduce operating costs by up to 45% in comparison with First-Fit-Decreasing heuristic method as a baseline algorithm.