Intelligent Mobile Edge Computing: A Deep Learning Based Approach

In recent times researchers across the globe have shown keen interest towards advancements in the domain of edge computing. Mobile Edge Computing (MEC) is a new age computing paradigm wherein cloud services are made accessible at network edges via the use of mobile base stations. It is a promising technology that helps in overcoming the limitations of mobile cloud computing. MEC facilitates seamless integration of various application services, thereby proving cloud resources at the edge of the network, within the vicinity of the end-user’s locality. It can effortlessly be integrated with the upcoming 5G architecture, hence supporting the execution of resource-rich applications that require low network latency. In order to enhance the levels of intelligence at mobile base stations, deep learning algorithms can be implemented over network edges for rendering optimized communication and workload balancing. The paper discusses a conceptual architecture for creating a mobile edge computing environment involving the applicability of deep learning algorithms. The paper discusses the fundamentals of MEC along with specific applications of reinforcement and continuous learning in an edge environment. We list the benefits of MEC along with a discussion on how its amalgamation with deep learning models can prove beneficial in case of a computation offloading scenario.