Energy Efficient Resource Allocation in Cloud Computing Environments

Abstract

Power consumption is one of the major concerns for the cloud providers. The issue of disorganized power consumption can be categorized into two main groups: one caused by server operations and one occurred during the network communications. In this paper, a platform for virtual machine (VM) placement/migration is proposed to minimize the total power consumption of cloud data centers (DCs). The main idea behind this paper is that with the collaboration of optimization scheduling and estimation techniques, the power consumption of DC can be optimally lessened. In the platform, an estimation module has been embedded to predict the future loads of the system, and then, two schedulers are considered to schedule the expected and unpredicted loads, respectively. The proposed scheduler applies the column generation technique to handle the integer linear/quadratic programming optimization problem. Also, the cut-and-solve-based algorithm and the call back method are proposed to reduce the complexity and computation time. Finally, numerical and experimental results are presented to validate our findings. Adaptation and scalability of the proposed platform result in a notable performance in VM placement and migration processes. We believe that our work advances the state of the art in workload estimation and dynamic power management of cloud DCs, and the results will be helpful to cloud service providers in achieving energy saving.