Deakin University

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Graph4Edge: a graph-based computation offloading strategy for Mobile-Edge workflow applications

conference contribution
posted on 2020-01-01, 00:00 authored by Lingmin Fan, Xiao LiuXiao Liu, Xuejun Li, Dong Yuan, Jia Xu
Mobile Edge Computing (MEC) expands the capability of mobile devices so that users can run complicated and computation-intensive applications such as workflow and machine learning tasks. Computation offloading is the key technology for MEC and has attracted a lot of research efforts in recent years. However, most of the existing studies employed optimisation algorithms such as GA and PSO which have significant computation overhead. Meanwhile, the computation tasks are often assumed to be independent of each other, which is not applicable to workflow applications with strong task dependencies. To address these issues, we propose Graph4Edge which is a graph-based computation offloading strategy for mobile-edge workflow applications. In this paper, firstly, we formulate the computation offloading problem in MEC using a DAG (Directed Acyclic Graph) based model; secondly, we propose the shortest-path-based algorithm to find the optimal computation offloading plan; finally, preliminary experiments with real-world workflow traces are conducted to evaluate the performance of our proposed strategy. Given the promosing results demonstrated in this paper, we have also presented some important research directions for our future work.



IEEE Computer Society. International Conference (18th : 2020 : Austin, Texas)


IEEE Computer Society International Conference


1 - 4


Institute of Electrical and Electronics Engineers


Austin, Texas

Place of publication

Piscataway, N.J.

Start date


End date






Publication classification

E1 Full written paper - refereed



Title of proceedings

PerCom Workshops : Proceedings of the 2020 IEEE International Conference on Pervasive Computing and Communications Workshops