EV-Assisted Computing for Energy Cost Saving at Edge Data Centers

Geo-distributed edge data centers (EDCs) are expected to handle a large portion of tasks offloaded from cloud data centers for various emerging edge services. However, the high energy consumption and cost add a huge burden to edge service providers (ESPs). This presents a unique challenge as traditional energy-saving strategies applicable in cloud data centers fail to apply to EDCs, given the latency-sensitive nature of edge services. In response, we put forward an innovative electric vehicle (EV)-assisted edge computing architecture that leverages idle computing resources and stored energy of EVs. Our design aims to decrease energy expenditures for ESPs by choosing EVs with more economical service costs to handle a portion of the edge services during critical periods. We construct an energy cost-aware workload offloading model and discretize the original model into multiple small-scale solvable forms in both temporal and spatial dimensions. Furthermore, we reconfigure the Kuhn-Munkres algorithm to produce an online joint matching solution to counter QoS decline, generating a mutually advantageous situation for ESPs and EV participants. Upon experimentation with real-world traces, our design demonstrates a significant reduction in total energy cost (up to 31%) and offers considerable incentives for EV participants.