A Computation- and Network-Aware Energy Optimization Model for Virtual Machines Allocation

Reducing energy consumption in cloud data center is a complex task, where both computation and network related effects must be taken into account. While existing solutions aim to reduce energy consumption considering separately computational and communication contributions, limited attention has been devoted to models integrating both parts. We claim that this lack leads to a sub-optimal management in current cloud data centers, that will be even more evident in future architectures characterized by Software-Defined Network approaches. In this paper, we propose a joint computation-plus-communication model for Virtual Machines (VMs) allocation that minimizes energy consumption in a cloud data center. The contribution of the proposed model is threefold. First, we take into account data traffic exchanges between VMs capturing the heterogeneous connections within the data center network. Second, the energy consumption due to VMs migrations is modeled by considering both data transfer and computational overhead. Third, the proposed VMs allocation process does not rely on weight parameters to combine the two (often conflicting) goals of tightly packing VMs to minimize the number of powered-on servers and of avoiding an excessive number of VM migrations. An extensive set of experiments confirms that our proposal, which considers both computation and communication energy contributions even in the migration process, outperforms other approaches for VMs allocation in terms of energy reduction.