Resource-Aware Decentralized Adaptive Computational Offloading & Task-Caching for Multi-Access Edge Computing

Abstract

Smart technologies or IoT devices have been designed to execute intensive applications that request more computational and other computer system resources. However, those devices have a resource constraint. To address the challenge, we adopt Multi-Access Edge Computing which is a new paradigm that transforms and localize Cloud services and capabilities at the Edge of Radio-Access Network based on proximity for mobile subscribers. In this paper, we proposed a Resource-Aware Decentralized Computing and Caching framework for Multi-Access Edge Computing. So, smart end-user devices work collaboratively and independently with resourceful edge devices or peer devices in close proximity during the unreliable network. Moreover, those devices can offload intensive application or access completed cached tasks to provide efficient resource utilization & Quality of User Experience. The drawback is expressed based on Non-Cooperative Game Theory which is NP-hard to solve and we show that the game concedes a Nash Equilibrium. Our Scheme optimizes computational and storage resources efficiently. We have done exhaustive observation the outcome shows that our scheme provides better performance than the conventional scheme in terms of enhanced storage capability, high Quality of User Experience, and low energy consumption.