Dependency-Aware Computation Offloading for Mobile Edge Computing With Edge-Cloud Cooperation

Most of existing Multi-access edge computing (MEC) studies consider the remote cloud server as a special edge server, the opportunity of edge-cloud collaboration has not been well exploited. We propose a dependency-aware offloading scheme in MEC with edge-cloud cooperation under task dependency constraints. Each mobile device has a limited budget and has to determine which sub-task should be computed locally or should be sent to the edge or remote cloud. To address this issue, we divide the offloading problem into two application finishing time minimization sub-problems with two different cooperation modes, both of which are proved to be NP-hard. We then devise one greedy algorithm with approximation ratio of
$1+\epsilon$
for the first mode with edge-cloud cooperation but no edge-edge cooperation. Then we design an efficient greedy algorithm for the second mode, considering both edge-cloud and edge-edge co-operations. Extensive simulation results show that for the first mode, the proposed greedy algorithm achieves near optimal performance for typical task topologies. On average, it outperforms the modified Hermes benchmark algorithm by about
$23\%\sim 43.6\%$
in terms of application finishing time with given budgets. By further exploiting collaborations among edge servers in the second cooperation mode, the proposed algorithm helps to achieve over 20.3 percent average performance gain on the application finishing time over the first mode under various scenarios. Real-world experiments comply with simulation results.