Computational task off-loading using deep Q-learning in mobile edge computing

Because of the growing proliferation of networked Inter of Things (IoT) devices and the demanding requirements of IoT applications, existing cloud computing (CC) architectures have encountered significant challenges. A novel mobile edge computing (MEC) can bring cloud computing capabilities to the edge network and support computationally expensive applications. By shifting local workloads to edge servers, it enhances the functionality of mobile devices and the user experience. Computation off-loading (CO) is a crucial mobile edge computing technology to enhance the performance and minimize the delay. In this paper, the deep Q-learning method has been utilized to make off-loading decisions whenever numerous workloads are running concurrently on one user equipment (UE) or on a cellular network, for better resource management in MEC. The suggested technique determines which tasks should be assigned to the edge server by examining the CPU utilization needs for each task. This reduces the amount of power and execution time needed.