Optimization of computational offloading in the mobile edge: a game theoretic approach

With the rise of Fifth-Generation (5 G) networks, smart applications are becoming increasingly computation-intensive and latency-sensitive. To address these challenges, this paper introduces a novel intelligent broker-based architecture to manage distributed computing and dynamic workloads in Mobile Edge Computing (MEC) environments. Additionally, we propose a duopoly competition model to analyze the impact of price, Quality of Service (QoS) in terms of response time, and security on task offloading. A crucial aspect of this work is the use of the Price of Anarchy (PoA) to evaluate the effects of selfish behavior among Edge Providers (EPs) on system performance. Experimental results show that the proposed model quickly converges to a stable Nash Equilibrium (NE), defining an optimal offloading scheme that balances cost, service quality, and security, while ensuring fair competition among EPs.