With the development of the Internet of Things (IoT) and Information Communication Technology (ICT), a large number of computation-intensive terminal applications have emerged. Multi-access Edge Computing (MEC) is a promising technology to meet terminal devices’ computational and battery-capacity demands, yet it also generates significant energy consumption and carbon emissions. This paper investigates the dynamic task offloading problem in Energy Harvesting (EH)-assisted edge-cloud systems. We aim to minimize the overall carbon emissions of the system while ensuring long-term stability and satisfying multi-user, multi-server constraints by optimizing CPU frequencies, offloaded task sizes, and offloading decisions. Specifically, we formulate the problem as a multi-slot stochastic optimization problem, which is NP-hard. Employing a stochastic optimization framework, we transform and decompose the problem into multiple subproblems that can be solved efficiently. Considering the coupling between offloading decisions and resource allocation, we propose the Carbon-Aware Resource Allocation and Offloading (CARAE) algorithm to obtain an efficient carbon-aware task offloading strategy. A series of theoretical analyses and simulations are conducted to verify that the proposed CARAE algorithm effectively reduces overall carbon emissions while maintaining system performance.
Fu et al. (Wed,) studied this question.