Smart Grid Powered Datacenters for Computation Offloading: Delay-Sensitive Scheduling and Distributed Protocols - Tian, Di; Chen, Wei; Han, Yuxing (IEEE International Conference on Communications (2003), 2025)
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). As a result, efficient and timely offloading of computational tasks such as model training in smart grid powered datacenters becomes a challenging but yet critical issue that has attracted considerable recent attention. In particular, our aim is to minimize the overall cost given the time-varying power supply along with its price induced by the dynamic nature of renewable energy, while satisfying a delay constraint that assures the quality-of-service (QoS). To achieve this goal, we present a mixed integer programming (MIP) for cost-efficient and latency-sensitive computation offloading in smart grid powered datacenters. 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. 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.