Multimedia Computational Offloading for 5G Mobile Edge Computing

Abstract Due to the tremendous growth of smartphone users with their massive usage of faster response, delay sensitive applications lead to high traffic demands, still not yet been met by current researchers. Thus, the ever-increasing computational demands are managed through computational offloading, which offload intensive workloads to the small cells having functionality similar to the resource-rich providers namely edge server. With the use of edge server, the heavily loaded computations get executed outside of the mobile device which results in minimization of mobile energy consumption and latency. Mobile edge computing is an emerging paradigm of commercial infrastructure for computational offloading, which enhances the power of smart mobile devices. Generally, edge provides cloud services and resources to the nearest proximity of users with radio access in fifth-generation (5G) networks for low latency, prompt response, and filtering. But, the frequent network failure automatically degrades the performance. Hence in this paper, we integrate the functionalities of Edge Server with Pico cells (ESP) for efficient traffic management and also propose a distributed Multi hop Mesh Middle layer (MMM) architecture for seamless communication with high resilience, minimal latency, and reduction of energy consumption. Generally, a pico cell is a distributed antenna system, an alternative to a repeater used to extend wireless services to 100 users. We developed an Ant Social based Vector (ASV) optimization algorithm for an intelligent offload decision, which paves the way for backhaul routing conflicts. Simulation results of Cloud Sim show that offloading tiny devices integrated with significant additional computational capabilities would satisfy all the demands from each network within a 15ms delay.