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

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.