Edge-cloud collaboration for low-latency, low-carbon, and cost-efficient operations

Abstract

The growing demand for low-latency services and the increasing impact of carbon emissions pose challenges to traditional cloud computing architectures. Hence, to address the high latency limitations of traditional cloud computing and leverage the advantages of abundant renewable energy sources (RESs) and low-priced electricity of remote clouds, we design an edge-cloud collaboration system to distribute mixed workloads, aiming at meeting delay requirements while reducing carbon emissions and improving operating profits. Specifically, delay-sensitive workloads are allocated to nearby edge clouds, while delay-tolerant workloads are assigned to remote core clouds. Additionally, a multi-level scheduling strategy is proposed to flexibly allocate delay-tolerant workloads. Beyond responding to RES generation and electricity price signals, this strategy prioritizes workloads and reduces the supply of high-priced electricity to low-priority workloads, further decreasing electricity costs. Finally, we use Alibaba workload traces to evaluate the proposed strategy. Simulation results demonstrate that the proposed edge-cloud collaboration system can reduce the average response delay of delay-sensitive workloads by 33.42 times compared to the traditional cloud system. Additionally, compared to the effective energy storage systems (ESSs)-based algorithm, the proposed strategy not only reduces carbon emissions by 3.14% but also increases operating profits by 18.78%. These results highlight its potential to enhance environmental sustainability, economic benefits, and Quality of Service (QoS).