Joint Minimization of the Energy Costs From Computing, Data Transmission, and Migrations in Cloud Data Centers

We propose a novel model, called joint computing, data transmission and migration energy costs (JCDME), for the allocation of virtual elements (VEs), with the goal of minimizing the energy consumption in a software-defined cloud data center (SDDC). More in detail, we model the energy consumption by considering the computing costs of the VEs on the physical servers, the costs for migrating VEs across the servers, and the costs for transferring data between VEs. In addition, JCDME introduces a weight parameter to avoid an excessive number of VE migrations. Specifically, we propose three different strategies to solve the JCDME problem with an automatic and adaptive computation of the weight parameter for the VEs migration costs. We then evaluate the considered strategies over a set of scenarios, ranging from a small sized SDDC up to a medium-sized SDDC composed of hundreds of VEs and hundreds of servers. Our results demonstrate that JCDME is able to save up to an additional 7% of energy with respect to previous energy-aware algorithms, without a substantial increase in the solution complexity.