Joint Power Scaling of Processing Resources and Consolidation of Virtual Network Functions

With the advent of network 'softwarization', Network Functions Virtualization (NFV) is foreseen to provide flexibility and programmability levels that would essentially help in coping with tomorrow's demands. However, energy efficiency and the resulting complexity of network/service management pose serious sustainability and scalability issues that may hinder NFV's advantages. This paper considers these aspects in the context of datacenter networks. We propose an energy-Aware resource allocation scheme to manage virtual machines, dedicated to perform certain (virtualized) network functions, among a pool of energy-Tunable physical resources (processors/cores). We use online measurements to periodically estimate some statistical features of the offered workloads by considering a fairly general renewal model that captures traffic bustiness and hardware operational settings. Then, resources are dynamically managed by jointly performing power scaling and in-server consolidation according to the actual workload variations. The average power consumption generated by this strategy is evaluated and compared with that of a classical bin-packing consolidation, over processors running always with the highest-performance configuration. Results show that the proposed approach can reduce the average power consumption of the datacenter by up to 10%, suggesting a considerable amount of annual savings