An optimization model to reduce energy consumption in software-defined data centers

Abstract

The increasing popularity of Software-Defined Network technologies is shaping the characteristics of present and future data centers. This trend, leading to the advent of Software-Defined Data Centers, will have a major impact on the solutions to address the issue of reducing energy consumption in cloud systems. As we move towards a scenario where network is more flexible and supports virtualization and softwarization of its functions, energy management must take into account not just computation requirements but also network related effects, and must explicitly consider migrations throughout the infrastructure of Virtual Elements (VEs), that can be both Virtual Machines and Virtual Routers. Failing to do so is likely to result in a sub-optimal energy management in current cloud data centers, that will be even more evident in future SDDCs. In this chapter, we propose a joint computation-plus-communication model for VEs allocation that minimizes energy consumption in a cloud data center. The model contains a threefold contribution. First, we consider the data exchanged between VEs and we capture the different connections within the data center network. Second, we model the energy consumption due to VEs migrations considering both data transfer and computational overhead. Third, we propose a VEs allocation process that does not need to introduce and tune weight parameters to combine the two (often conflicting) goals of minimizing the number of powered-on servers and of avoiding too many VE migrations. A case study is presented to validate our proposal. We apply our model considering both computation and communication energy contributions even in the migration process, and we demonstrate that our proposal outperforms the existing alternatives for VEs allocation in terms of energy reduction.