Dynamic task offloading in edge computing for computer access point selection based on adaptive deep reinforcement learning with meta-heuristic optimization

Abstract

The computationally intensive tasks are processed by mobile devices which include data processing, virtual reality, and artificial intelligence. The computational resources of the mobile devices are very low so they are suited to perform all tasks with low latency. Mobile Edge Computing (MEC) is a cutting-edge computing model that offloads computation-intensive tasks to MEC servers to increase the capability of computing in Mobile Devices (MDs). Due to the extensive use of Wireless Local Area Networks (WLAN), each MD can use numerous Wireless Access Points (WAPs) to offload tasks to a server. In this research work, the task offloading problem is determined by considering the delay-sensitive task along with edge load dynamics to reduce the long-term cost. The distributed algorithm based on Adaptive Deep Reinforcement Learning (ADRL) is introduced, where every device is analyzed for offloading decisions without knowing the task model of other devices. The parameters in the model are optimized using the Fitness-based Piranha Foraging Optimization Algorithm (F-PFOA) to enhance the performance of the model. Finally, the evaluation is done by using the various metrics to showcase the effectiveness of the proposed model, and it gives the throughput is 93.5, which is enhanced than other existing models. Thus, the simulation outcome with a greater number of mobile devices and corresponding edge nodes showed that the developed optimization minimizes the dropped task’s ratio and average task delay respectively. The result of the designed model outperformed better than other available models.