Title: Combined jelly-snake optimisation with deep learning architecture for task offloading and resource allocation in edge computing

Authors: A. Raja; P.M. Prathibhavani; K. Rajuk Venugopal

Addresses: Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, K.R. Circle, Bengaluru, Karnataka, 560001, India ' Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, K.R. Circle, Bengaluru, Karnataka, 560001, India ' Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, K.R. Circle, Bengaluru, Karnataka, 560001, India

Abstract: Edge computing allows devices to transfer their computational tasks to nearby edge servers. However, the effectively managing offloading decisions and optimising resource remains a challenge. To tackle this issue, this paper proposes an innovative method for task offloading and resource allocation in edge computing. In this, CNN-based task offloading method is utilised, incorporates WBAN, ES, and a medical centre integrated into edge computing for offloading structure. The GUN handles task-related data scaling, which serves as input for CNN. The CNN produces result ranging from zero to one, where '0'-local task execution and '1'-task offloading to ES. Then, hybrid algorithm combines JSO and SOA is proposed to effectively manage workload demands. The SUJO method optimises resource allocation by considering factors-makespan, task priority, execution time, and energy consumption. The comparative analysis demonstrates SUJO's superiority, achieving execution time of less than 50 seconds and proves effective for optimising task offloading and resource allocation.

Keywords: wireless body area network; WBAN; gateway user node; GUN; edge computing; resource allocation; task offloading; convolutional neural network; CNN; snake updated with jellyfish optimisation algorithm; SUJOA.

DOI: 10.1504/IJWET.2025.146742

International Journal of Web Engineering and Technology, 2025 Vol.20 No.2, pp.146 - 176

Received: 11 Mar 2024
Accepted: 13 Sep 2024
Published online: 16 Jun 2025 *

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