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Open Access

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

College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
School of Software Technology, Zhejiang University, Ningbo 315048, China
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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.



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Tsinghua Science and Technology
Pages 1872-1889
Cite this article:
Pang S, Zhao X, Luo J, et al. Maximizing Overall Service Profit: Multi-Edge Service Pricing as a Stochastic Game Model. Tsinghua Science and Technology, 2024, 29(6): 1872-1889.








Web of Science






Received: 10 January 2024
Revised: 17 February 2024
Accepted: 01 March 2024
Published: 20 June 2024
© The Author(s) 2024.

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