Long Chen, Jigang Wu, Jun Zhang, Hong-Ning Dai, Xin Long, Mianyang Yao
IEEE Transactions on Cloud Computing (accepted to appear)
Publication year: 2022

Abstract

Most of existing mobile 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. In this paper, 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-tasks should be computed locally or should be sent to the edge or remote cloud. To address this issue, we first formulate the offloading problem as an application finishing time minimization problem with two different cooperation modes, both of which are proven 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% ~ 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% average performance gain on the application finishing time over the first mode under various scenarios. Real-world experiments comply with simulation results.

Bibtex

@ARTICLE{LChen:TCC20,
author={Long Chen and Jigang Wu and Jun Zhang and Hong-Ning Dai and Xin Long and Mianyang Yao},
journal={IEEE Transactions on Cloud Computing},
}