An Energy-Aware Algorithm for Optimizing Resource Allocation in Software Defined Network

With the increasing popularity of cloud computing, the huge amount of energy consumed by data centers has gained much attention. Current proposals address the energy efficiency problem by two major methods: optimizing the allocation of physical servers and network elements (routers or switches). In order to improve resource utilization and minimize the energy consumption, the former method focuses on virtual machine (VM) placement regardless of the inherent traffic between VMs. The latter designs power saving routing and flow scheduling while this method neglects the resource demands in VMs. In this paper, we jointly consider the VM placement and network routing to optimize energy cost. In addition, we take advantage of the centralized and global controller in Software Defined Networking (SDN) paradigm. Inspired by the idea of Data Field, we propose a novel algorithm to evaluate the importance and relationships among multiple VMs. The potential score based on Data Field is a more accurate global ranking considering the resource demands and inter-VM traffic. Extensive simulations are conducted on different scales of typical data center topologies, such as Fat-Tree and BCube. Results show that our proposal can reduce the number of active devices including the servers and network elements, and thereby saves power consumption. Moreover, the proposed algorithm improves network performance by decreasing the average hops of per flow.