Gradient optimisation for network power consumption

The purpose of this paper is to examine how a gradient-based algorithm that minimises a cost function that includes both quality of service (QoS) and power minimisation in wired networks can be used to improve energy savings with respect to shortest-path routing, as well as against a "smart" autonomic algorithm called EARP which uses adaptive reinforcement learning. Comparisons are conducted based on the same test-bed and identical network trac. We assume that due to the need for network reliability and resilience we are not allowed to turn o routers and link drivers. We also assume that for QoS reasons (notably with regard to jitter and to avoid packet desequencing) we are not al- lowed to split trac from the same ow into dierent paths. Under these assumptions and for the considered trac, we observe that power con- sumed with the gradient-optimiser is a few percent to 10% smaller than that consumed using shortest-path routing or EARP. Since the magni- tude of the savings is small, this suggests that further power savings may only be obtained if under-utilised equipment can be dynamically put to sleep or turned on