Carbon-aware Enterprise Network through Redesign

We present a novel green communication framework incorporating the redesign of existing campus enterprise network (CEN) to offset the carbon emissions at the backbone based on the theory of data encoding and power spectral density (PSD) estimations. CEN is viewed as a collection of service nodes distributed in various buildings, wherein the nodes are flexible to be clustered for serving a specific application. The carbon-offset at the backbone is indispensable as it is often susceptible to heavy traffic flow, thereby necessitating high-power cooling equipment to reduce the intensive heat spots generated from the equipment, such as routers and servers. The proposed framework accounts carbon emission from the average power consumed by the transmitted data through integrating the area under PSD curve of the encoded-transmitted data. The redesign problem is formulated as an optimization problem to redistribute the heavily communicating nodes at the backbone and to dispense the heat spots away from the backbone. We have utilized two bio-inspired algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA) to search the redesign space. The simulation results with the Manchester encoder within GA offer a maximum reduction of 23.6% of annual carbon emissions when compared with the initial CEN, whereas the SA reduces the carbon emission by 16%.