Energy-Efficient and QoS-Guaranteed Distributed Scheduling for Computing Power Networks Powered by Smart Grids

Computing power networks (CPN) powered by smart grids are expected to play a vital role in the era of artificial intelligence (AI), because datacenters are now experiencing a dramatic increase in power consumption, especially with the vast deployments and applications of large language models (LLM). Consequently, efficient and low-latency offloading of computational tasks such as model training in smart grid powered CPN becomes a challenging but yet critical issue that has attracted considerable recent attention. In this paper, we aim at optimizing the energy efficiency of CPN with quality-of-service (QoS) requirements. To achieve this goal, we present a mixed integer programming (MIP) to minimize the overall cost given the time-varying power supply along with its price induced by the dynamic nature of renewable energy. A distributed algorithm for solving the MIP is judiciously conceived, which allows the datacenters and data owners to efficiently schedule the transmission of raw data and the computation for model training in a decentralized manner under a delay constraint guaranteeing QoS. Simulations demonstrate that the distributed protocol is capable of adapting to the dynamic energy price, time-varying supply of renewable energy, and even the line outage in smart grids with negligible latency and small signaling overhead.