PESA: a predictive energy‐saving approach based on an OSHMM

Abstract

Reducing energy consumption of the internet becomes more and more important. The authors propose a novel approach, called predictive energy-saving approach (PESA), to put near-idle nodes of a network into sleep mode to save energy. In PESA, they propose an online spatial hidden Markov model (OSHMM) to describe the distribution of the network traffic and predict the lowest-loaded nodes. Those nodes will be put into sleep mode under the constraints that the network's full connectivity is kept and no new congested nodes are generated. To reduce the computation, they leverage on an online training technique and M-algorithm in OSHMM. They evaluate the approach over a randomly generated topology and a real ISP backbone topology with a real traffic dataset. The results show that the proposed approach is energy efficient with low computation complexity.