Abstract
Computational offloading is a strategy by which mobile device (MD) users can access the superior processing power of a Multi-Access Edge Computing (MEC) server network. In this paper, we contribute a model of a system that consists of multiple MEC servers and multiple MD users. Each MD has multiple computational tasks to perform, and each task can either be computed locally on the MD, or it can be offloaded to one of the MEC servers. For this system and having global knowledge, we compute the theoretical optimal allocation that minimises the time required to complete the computation of all tasks. Subsequently, we contribute a distributed heuristic algorithm that allows each MD to independently, and using local knowledge only, decide how to handle each individual job. Furthermore, we propose three approaches to decide whether to offload each individual job, and three mechanisms to determine which MEC server each task should be offloaded to. We use simulations to evaluate those approaches in terms of how well they can approximate the theoretical optimum. The proposed heuristic algorithm is tested on a range of experiments, and the results demonstrate that the heuristic algorithm can produce reasonable quality solutions.
Original language | English |
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Title of host publication | 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications |
Publisher | IEEE Computer Society |
Volume | 31 |
DOIs | |
Publication status | Published - 3 Sept 2020 |
Event | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2020 - Online Duration: 31 Aug 2020 → 3 Sept 2020 https://pimrc2020.ieee-pimrc.org/ |
Conference
Conference | IEEE International Symposium on Personal, Indoor and Mobile Radio Communications 2020 |
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Abbreviated title | IEEE PIMRC 2020 |
Period | 31/08/20 → 3/09/20 |
Internet address |
Keywords
- Multi-Access Edge Computing
- computation offloading
- heuristic algorithm
- theoretical optimal