Enhancing Service Offloading for Dense Networks Based on Optimal Stopping Theory in Virtual Mobile Edge Computing

The present and next-generation networks of service offloading present exciting issues. The traditional network optimisation techniques still require rigorous heuristic tuning to obtain a good result. Traditional methods employ data as an input and produce solutions that are close to ideal. These methods only operate with tiny networks and exhibit exponential computation times. Thus, we are driven to understand the behaviour of conventional optimisation methods while boosting service quality and meeting next-generation applications. The Optimization problem in Virtual Mobile Edge Computing using Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading (TD3PG-ICO). Optimal Stopping Theory is used in virtual mobile edge computing for the best service offloading (OST). To support the use case, a service offloading protocol is also described. We use Software Defined Networking (SDN) and Network Function Virtualization (NFV) ideas to manage and virtualise network components. To deal with dense Internet of Things (IoT) networks, a TD3PG-OST-based offloading is therefore suggested. There are extensive analyses and comparisons with cutting-edge methods. Results demonstrate the effectiveness of the proposed methods in terms of networking, resource utilisation, and service offloading.