Spatiotemporal Task Scheduling for Green Computing in Computing Power Networks

Recently, the advancement of information technologies have accelerated the generation of big data, necessitating substantial computing power. This has spurred the development of Computing Power Networks (CPNs), which can overcome the limitations of computing power isolation. However, CPNs consume significant energy and produce large carbon emissions during big data processing. Therefore, an energy-efficient task scheduling scheme, coupled with the utilization of renewable energy, appears to be particularly necessary. Nevertheless, the interplay between computing power and networks, and the spatiotemporal variations in green CPNs pose a challenge to designing the task scheduling scheme. In this paper, we propose a transferable spatiotemporal task scheduling scheme with a triple selection of CPN nodes, routing paths, and forwarding time of tasks. The scheme can overcome the dynamics of green CPNs, and jointly optimize the energy consumption and carbon emissions with ensuring delay constraints and long-term load balancing. Then, we present a task scheduling algorithm based on improved nondominated sorting genetic algorithm-II (NSGA-II) to solve the problem, and numerical results demonstrate that our scheme is effective in reducing the overall energy consumption and carbon emissions of CPNs.