A Hybrid Artificial Neural Network for Task Offloading in Mobile Edge Computing

TL;DR

This paper introduces a hybrid ANN-based task offloading method for mobile edge computing that predicts suitable edge computers for tasks, improving response times and outperforming existing approaches.

Contribution

It presents a novel hybrid ANN model for task offloading that clusters edge computers and predicts optimal resources, enhancing efficiency in edge computing environments.

Findings

Outperforms state-of-the-art machine learning approaches

Reduces task response time in edge computing

Effective clustering of edge devices based on hardware features

Abstract

Edge Computing (EC) is about remodeling the way data is handled, processed, and delivered within a vast heterogeneous network. One of the fundamental concepts of EC is to push the data processing near the edge by exploiting front-end devices with powerful computation capabilities. Thus, limiting the use of centralized architecture, such as cloud computing, to only when it is necessary. This paper proposes a novel edge computer offloading technique that assigns computational tasks generated by devices to potential edge computers with enough computational resources. The proposed approach clusters the edge computers based on their hardware specifications. Afterwards, the tasks generated by devices will be fed to a hybrid Artificial Neural Network (ANN) model that predicts, based on these tasks, the profiles, i.e., features, of the edge computers with enough computational resources to execute them. The predicted edge computers are then assigned to the cluster they belong to so that each task is assigned to a cluster of edge computers. Finally, we choose for each task the edge computer that is expected to provide the fastest response time. The experiment results show that our proposed approach outperforms other state-of-the-art machine learning approaches using real-world IoT dataset.