Research Publications

An AI-Assisted Model for Task Offloading Decision Making in Edge Computing

Edge computing (EC) serves as a promising complement to cloud computing, yet the challenge of task offloading decision-making persists. While task offloading can extend device lifespan, it may introduce delays that surpass acceptable application thresholds. To address this challenge, we propose a machine learning (ML) technique that leverages key factors such as network latency, battery level, and device location to inform task offloading decisions. This novel ML technique, which is exclusively presented in this paper, holds potential for enhancing task offloading decision-making. Furthermore, the paper discusses future work, including testing and validating the proposed technique, as well as conducting an investigation into the impact of varying the number of mobile users within an edgeenabled environment.

https://hal.science/hal-04219825/document

Messaoud Babaghayou

Messaoud Babaghayou

PhD in computer science (Location Privacy in IoV). PhD student (Energy Efficiency in Mobile Edge Computing) at Université Amar Telidji Laghouat.
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