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Energy-Aware and Fair Multi-User Multi-Task Computation Offloading
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AI
DOI：10.1109/ICNC57223.2023.10074189.png)
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Abstract
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Enabling computation intensive tasks with low latency requirements on mobile devices considering their battery life time is one of the challenges in 5G networks. 5G is an era with massive device connectivities, therefore, a realistic offloading scenario should consider a dense and dynamic scenario with users' movements. Considering the powerful Central Processing Units (CPUs) of current mobile devices and Device-to-Device (D2D) communications, the available resources of adjacent devices can be exploited to fulfil the requirements of recent applications. However, in order to prevent over-exploiting behaviours of some users in a feasible cooperation offloading scenario, defining proper incentive mechanisms is a must. In this work, we propose a fair energy aware offloading framework considering the inter dependency relationships of computation tasks. Our scenario contains a dense small cell with a MEC attached to the base station (BS). In order to solve our mixed integer nonlinear offloading problem (MINLP), a genetic algorithm is employed. The simulation results show a significant reduction in used energy as well as completion of the tasks within the respective deadlines compared to the other benchmarks.
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