Design and Analysis of Heuristic Algorithms for Energy-Constrained Task Scheduling With Device-Edge-Cloud Fusion

Mobile edge computing with device-edge-cloud fusion provides a new type of heterogeneous computing environment. We consider task scheduling with device-edge-cloud fusion (without energy concern) and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems. The main contributions of the paper are summarized as follows. We design three heuristic algorithms for task scheduling with device-edge-cloud fusion and prove an asymptotic performance bound. We design one heuristic algorithm for energy-constrained task scheduling with device-edge-cloud fusion, which solves the two subproblems of task scheduling and power allocation in an interleaved way. We derive lower bounds for the optimal solutions for both task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion, so that the performance of our heuristic algorithms can be compared with that of an optimal algorithm. We experimentally evaluate the performance of our heuristic algorithms and find that the performance of our heuristic algorithms are very close to that of optimal algorithms. To the best of our knowledge, this is the first paper which studies task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems and conducts analytical performance evaluation.