Exploiting CPU-load and data correlations in multi-objective VM placement for geo-distributed data centers

Cloud computing has been proposed as a new paradigm to deliver services over the internet. The proliferation of cloud services and increasing users' demands for computing resources have led to the appearance of geo-distributed data centers (DCs). These DCs host heterogeneous applications with changing characteristics, like the CPU-load correlation, that provides significant potential for energy savings when the utilization peaks of two virtual machines (VMs) do not occur at the same time, or the amount of data exchanged between VMs, that directly impacts performance, i.e. response time. This paper presents a two-phase multi-objective VM placement, clustering and allocation algorithm, along with a dynamic migration technique, for geo-distributed DCs coupled with renewable and battery energy sources. It exploits the holistic knowledge of VMs characteristics, CPU-load and data correlations, to tackle the challenges of operational cost optimization and energyperformance trade-off. Experimental results demonstrate that the proposed method provides up to 55% operational cost savings, 15% energy consumption, and 12% performance (response time) improvements when compared to state-of-the-art schemes.