Maximizing Overall Service Profit: Multi-Edge Service Pricing as a Stochastic Game Model

Abstract

The diversified development of the service ecosystem, particularly the rapid growth of services like cloud and edge computing, has propelled the flourishing expansion of the service trading market. However, in the absence of appropriate pricing guidance, service providers often devise pricing strategies solely based on their own interests, potentially hindering the maximization of overall market profits. This challenge is even more severe in edge computing scenarios, as different edge service providers are dispersed across various regions and influenced by multiple factors, making it challenging to establish a unified pricing model. This paper introduces a multi-participant stochastic game model to formalize the pricing problem of multiple edge services. Subsequently, an incentive mechanism based on Pareto improvement is proposed to drive the game towards Pareto optimal direction, achieving optimal profits. Finally, an enhanced PSO algorithm was proposed by adaptively optimizing inertia factor across three stages. This optimization significantly improved the efficiency of solving the game model and analyzed equilibrium states under various evolutionary mechanisms. Experimental results demonstrate that the proposed pricing incentive mechanism promotes more effective and rational pricing allocations, while also demonstrating the effectiveness of our algorithm in resolving game problems.