Computation Offloading in MEC Using Deep Q-Learning Framework

Mobile-edge computing (MEC) has evolved as a potential parameter to increase and improve the computational capabilities of mobile devices by shifting heavy resource demanded operations to adjacent edge servers. MEC is a model of distributed computing that brings cloud computing abilities nearer to users and mobile devices at the network’s edge. MEC supports basically all applications and services with minimal delay and higher bandwidth, by exploiting computing capabilities located at the edge of the network. The architecture of MEC includes many components as well as layers that support optimal computation, storage, and networking functions. In this research we proposed a deep reinforcement learning (DRL) based approach to solve the online computation offloading problem in MEC environments. Our framework proposes a real-time dynamic computational task allocation between mobile devices and edge servers based on an adaptive mechanism considering parameters such as network settings, device constraints, and server workloads. System performance was demonstrated through thoroughly simulated scenarios and experimental results shows that the DRL-based approach effectively reduced latency while maximizing energy efficiency to enhance the user experience. These results thus exhibit the promise of advanced machine learning techniques, especially DRL for efficient adaptive computation in MEC environments.