Maximizing Efficiency: Relocation and Deduplication for Result Caching in Distributed and Collaborative Edge Computing Networks

With the rapid growth of internet of things (IoT) devices, edge computing has emerged as a crucial technology for delivering low-latency and resource-efficient services. However, the surge in edge computing capabilities poses challenges in efficiently managing result caching and deduplication to effectively utilize storage and processing resources. This study introduces a novel approach that harnesses an adaptive enhanced initiation genetic algorithm (AEIGA) for result deduplication/relocation in collaborative and distributed edge computing networks, with the goal of enhancing task offloading and result delivery. Our work proposes an optimization framework that integrates deduplication/relocation strategies to minimize redundancy, improve latency, and optimize storage across edge servers. The proposed AEIGA addresses the NP-hard nature of the formulated optimization problem by efficiently exploring the vast solution space to find optimal or near-optimal solutions. Simulation results demonstrate significant improvements of the proposed AEIGA in various network performance metrics. Our findings highlight the effectiveness of employing AEIGA for result deduplication/relocation in edge computing, offering a scalable solution to meet the escalating demands of IoT networks.