Efficient Service Selection and Pricing in Edge-Cloud Computing Markets

This is the authors' abstract. We don't add key points for this paper.

Cloud and edge computing service providers (SPs) provide heterogeneous computing services to users, which forms the computing market. However, users’ service selections among SPs are unbalanced, resulting in inefficient resource utilization. In this paper, we analyze users’ service selection behaviors and design efficient pricing mechanisms to optimize the social welfare of the computing market. Considering the huge number of users and heterogeneous service providers, users can hardly acquire complete information to make their decisions, and we model users’ interactions as a dynamic service selection evolutionary game. Analyzing the evolutionary stable state (ESS) of the game and designing efficient pricing mechanisms for edge-cloud computing markets are challenging due to the implicit relationship between prices and users’ service selection dynamics, and the heterogeneity of service providers and user populations. We first investigate a single-population scenario where users are homogeneous, for which we prove that the ESS is unique. We design a static pricing mechanism which depends on SPs’ marginal costs and computation capacities, and a dynamic pricing mechanism which also depends on SPs’ real-time congestion tax. We prove that under our pricing mechanisms, the ESS is the socially optimal state and is globally asymptotic stable. We then analyze the general multi-population scenario where users are heterogeneous, for which we prove that the ESS exists but may be not unique. We design a static pricing mechanism (and a dynamic pricing mechanism) which depends on SPs’ marginal costs and the social-optimal average delay costs (and the real-time average delay costs). We prove that under our pricing mechanisms, the socially optimal state is an ESS and is asymptotically stable. Simulation results validate the effectiveness of our designed pricing mechanisms.