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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Mohamed El Ghmary
SidiMohamed Ben Abdellah University
Computer Science & IT Electrical & Electronics Engineering Engineering

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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
International Journal of Electrical and Computer Engineering (IJECE) Vol 10, No 4: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (847.097 KB) | DOI: 10.11591/ijece.v10i4.pp3757-3766

Abstract

In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. 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. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, 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.
Virtual machine migration in MEC based artificial intelligence technique Ali OUACHA; Mohamed EL Ghmary
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 10, No 1: March 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v10.i1.pp244-252

Abstract

The whole world is inundated with smaller devices equipped with wireless communication interfaces. At the same time, the amount of data generated by these devices is becoming more important. The smaller size of these devices has the disadvantage of being short of processing and storage resources (memory, processes, energy,...), especially when it needs to process larger amounts of data. In order to overcome this weakness and process massive data, devices must help each other. A low-resource node can delegate the execution of a set of computionly heavy tasks to another machine in the network to process them for it. The machine with sufficient computational resources must also deposit the appropriate environment represented by the adapted virtual machine. Thus, in this paper, in order to migrate the virtual machine to an edge server in a mobile edge computing environment, we have proposed an approach based on artificial intelligence. More specifically, the main idea of this paper is to cut a virtual machine into several small pieces and then send them to an appropriate target node (Edge Server) using the ant colony algorithm. In order to test and prove the effectiveness of our approach, several simulations are made by NS3. The obtained results show that our approach is well adapted to mobile environments.
Co-Authors Ali OUACHA Mohammed Ouçamah Cherkaoui Malki Tarik Chanyour Youssef Hmimz