Time and Resource Constrained Offloading with Multi-task in a Mobile Edge Computing Node

Mohamed EL GHMARY, Youssef Hmimz, Tarik Chanyour, Mohammed Ouçamah CHERKAOUI MALKI


This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. In recent years, people interested in the telecommunications sector have noticed the importance of the theme of Mobile Edge Computing (MEC) along with the 5G, the Internet of Things (IoT) and virtualization of functions network. Currently, there are billions of connected objects around the world that generate continuous data, which must be stored and evaluated in real time for critical applications. This is a task that cloud solutions will not be able to control, while the implementation of demanding computer applications at the mobile device level is limited by battery capacity and execution time. One of the traditional ways to increase the life of mobile batteries and improve the quality of the user experience for computationally intensive and latency-sensitive applications is offloading some of these applications on the MEC. We consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decision and the allocation of IT resources to reduce the latency of tasks’ processing. Therefore, to decide tasks’ offloading, we developped a heuristic solution based on the simulated annealing algorithm, which can improve the offloading rate and reduce the total task latency while meeting short decision time. We performed a series of experiments to show its efficiency. Finally, the obtained results in terms of full-time treatrement are very encouraging. In addition, our solution makes offloading decisions within acceptable and achievable deadlines.


Mobile Edge Computing; Computation Offloading; Processing Time; optimization; Simulated Annealing.


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DOI: http://doi.org/10.11591/ijece.v10i4.pp%25p
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