Stochastic Congestion Game for Load Balancing in Mobile-Edge Computing

Mobile-edge computing can reduce task execution delay and improve the Quality of Experience (QoE) for the network edge users. However, when there are multiple independent cloudlets in the network with the mobile users offloading tasks randomly, how to maintain the load balancing of the independent cloudlets, how to improve the quality of service and users QoE are still issues need to be solved. To this end, we study these issues from the perspective of game theory and propose decentralized learning algorithms. First, we turn the cloudlets load-balancing issue into a competition that each user minimizes its task execution time, and then a stochastic congestion game with incomplete information is proposed. Second, based on the existence proof of the Nash equilibria by using potential game theory, we propose a multiuser decentralized learning algorithm to obtain the pure Nash equilibrium strategy of each user. Then, an ordinary differential equation is derived to prove the convergence of the algorithm. Finally, we propose two application scenarios, one is for static users and the other is for dynamic users, and then the performances of the algorithm in a static scenario is tested. In order to adapt to dynamic scenarios, and further improve the performance and reduce communication costs, we propose a decentralized learning algorithm with termination condition. The experiments show that this algorithm can improve the load balancing of the multicloudlet system, and enhance the quality of service.

Automated summary

The study addresses the load balancing issues of multiple independent cloudlets and proposes decentralized learning algorithms to improve service quality and user experience. Application scenarios for static and dynamic users are tested, with experiments showing improved load balancing and service quality.