Mobile edge computing (MEC) is a recent technology that brings the computing resources too closer to the network edge with reduced response rate in computationally intensive applications. With increasing mobile devices with limited computing and wireless capabilities, the response time of Mobile Switching Center (MSC) deteriorates. Therefore it is necessary to jointly manage the computing and wireless resources in order to avoid deterioration due to limited capabilities. Also, the joint optimisationtechniques limits its capability due to computational complexity and incompatible devices. To avoid all these limitations, in this paper, we propose a joint optimisation model that suitably optimizes the cloud and wireless resources in an optimal way, thereby reducing its response time. An equilibrium model is presented that aims to reduce the trade-offs between the resources and computing capabilities. A state-reward model is developed to maintain the equilibrium between the resources and computational capabilities. To improve the resources allocation capability, we use the reinforcement learning for better allocation of resources in MEC. The performance is compared with other existing methods to test the effectiveness of the model.
Volume 11 | 08-Special Issue
Pages: 608-614