Energy efficient task allocation and energy scheduling in green energy powered edge computing

Abstract The ever-increasing computation tasks and communication traffic have imposed a heavy burden on cloud data centers and also resulted in a significantly high energy consumption. To ease such burden, edge computing is proposed to explore the distributed resources of edge devices (e.g., base stations) to provision the cloud services for latency-sensitive applications at the network edge. Owing to the geo-distribution of edge devices, edge computing is also an ideal energy efficient platform to leverage the distributed green energy for energy efficient computing. Thus, it is natural to integrate Energy Internet (EI) technology into edge computing for customizable energy scheduling. In such EI supported edge computing, both the green energy generation rates and the data processing demands vary in different time and space. To pursue high energy efficiency, it is desirable to maximize the utilization of green energy so as to reduce the brown energy consumption. This requires careful task allocation and energy scheduling to match the energy provision and demand. In this paper, we investigate the energy cost minimization problem with joint consideration of VM migration, task allocation and green energy scheduling and prove its NP-hardness. To tackle the computation complexity, a heuristic algorithm approximating the optimal solution is proposed. Through extensive simulations, we show that the proposed algorithm can efficiently reduce brown energy consumption and perform much close to the optimal solution.