EFFICIENT COST AND ENERGY MANAGEMENT IN CLOUD DATA CENTERS

Abstract

We target the problem of managing the power states of the servers in a Cloud
Data Center (CDC) to jointly minimize the electricity consumption and the
maintenance costs derived from the variation of power (and consequently of
temperature) on the servers’ CPU. More in detail, we consider a set of virtual
machines (VMs) and their requirements in terms of CPU and memory across a set of
Time Slot (TSs). We then model the consumed electricity by taking into account the
VMs processing costs on the servers, the costs for transferring data between the VMs,
and the costs for migrating the VMs across the servers. In addition, we employ a
material-based fatigue model to compute the maintenance costs needed to repair the
CPU, as a consequence of the variation over time of the server power states. After
detailing the problem formulation, we design an original algorithm, called
Maintenance and Electricity Costs Data Center (MECDC), to solve it. Our results,
obtained over several scenarios from a real CDC, show that MECDC largely
outperforms two reference algorithms, which instead either target the load balancing
or the energy consumption of the servers