GRU-OptiCom: Revolutionizing Computation Offloading in Edge Computing through Meta-Reinforcement Learning with GRU

In the current scenario, mobile devices face the problem of lack of computational power which limits their capability of data processing. To solve this problem, the Multi-access Edge Computing (MEC) framework is an optimal solution that enables the mobile device to offload the heavy tasks to the connected edge servers. This offloading not only reduces the computational burden on mobile devices but also helps in reducing latency making the application real-time. The MEC framework is specifically designed to optimize the offloading process using advanced optimization techniques. To manage the computational burden, these strategies are essential in redistributing jobs between mobile devices and edge servers. To assess the performance of the framework we use a metric called Latency. Also we have done the comparative analysis between this framework and other known techniques like MRLCO , HEFT-based greedy algorithms etc. This comparison shows the superiority of the MEC framework in reducing latency and providing optimal load distribution. This research provides a novel approach to task offloading and also sets a new benchmark for future advancements in the field of mobile and edge computing. This research provides a novel MEC framework which acts as a cornerstone in the advancement of mobile computing technologies.