Edge Energy-Aware Offloading Strategy for Tasks with DAG Structure in Mobile Edge Computing

Mobile edge computing (MEC), as a new computing paradigm, has become an effective solution to solve the contradiction between computing-intensive applications and resource-starved mobile user equipment (UE). UE enables it to offload tasks to small cell base stations (SBSs). However, as the demand for computation-intensive tasks continues to grow, the energy consumption of SBSs also continues to increase. This paper is to minimize the energy consumption of SBSs. We model an application with dependent subtasks as a directed acyclic graph (DAG), and propose an optimized energy-aware task offloading strategy (EAOC) to effectively allocate computing resources of edge servers to applications. EAOC makes decision for tasks by balancing the finish time and energy consumption. The simulation results show that the proposed EAOC has significant energy-saving compared to existing algorithms.