Efficient Mobile Edge Computing for Better Computation Offloading and Real Time Task Prediction Using Deep Reinforcement Learning Algorithm

Effective Mobile Edge Computing (MEC) has become essential due to the quick spread of mobile devices and the rising demand for real-time applications. MEC lowers latency and improves performance by bringing processing resources closer to end users. In order to maximize computation offloading and real-time task prediction in MEC, this work explores the application of Deep Reinforcement Learning (DRL) methods. We want to overcome the difficulties presented by dynamic and diverse network environments by utilizing DRL to increase the effectiveness of task execution and resource management. Significant gains in task prediction accuracy, offloading efficiency, and overall system performance are demonstrated by our suggested method. The dynamic and varied nature of mobile surroundings presents obstacles for MEC, despite its potential, in compute offloading and real-time task prediction. To maintain low latency, manage resource utilization, and balance the load between edge and cloud servers, effective offloading decisions are essential. Furthermore, proactive resource allocation and management depend on the ability to forecast real-time workloads with accuracy. In order to improve computation offloading and real-time task prediction, this research investigates the use of Deep Reinforcement Learning (DRL) algorithms in MEC. Our suggested architecture uses deep reinforcement learning to forecast activities in real time and make adaptive decisions on offloading, thus enhancing system performance.