Research on Edge Cloud Resource Pricing Mechanism Based on Stackelberg Game

Abstract

Mobile edge computing (MEC) supports terminal devices to offload tasks or applications to edge cloud server for processing. Edge cloud server will consume local resources when processing external tasks, so it is particularly important to build a resource pricing mechanism that charges terminal devices to reward edge cloud. The existing pricing mechanism relies on the static pricing of intermediaries, and high cost and late processing of terminal tasks make it difficult to realize the effective utilization of edge cloud computing resources. Aiming at the above problems, this paper proposes an edge cloud resource pricing mechanism based on Stackelberg game. Firstly, in view of the local task shelving problem of terminal devices due to insufficient funds during resource pricing, an auxiliary mechanism including loans and incentives is proposed to realize the timely processing of terminal devices tasks. Secondly, four price-oriented factors that affect resource pricing are proposed, and two pricing schemes, consistency and elasticity, are formulated to improve the accuracy and efficiency of pricing and prepare for dynamic pricing. Then, in order to make the dynamic pricing between terminal devices and edge cloud directly, a resource pricing mechanism model based on Stackelberg game is built, and the resource demand and pricing problem is transformed into the problem of maximum revenue of edge cloud and minimum payment cost of terminal devices. Finally, through improved reinforcement learning SARSA (state action reward state action) algorithm, the optimal strategy of resource demand and pricing is obtained. Experiments show that the pricing mechanism proposed in this paper is more than 12% better than other pricing algorithms in terms of edge cloud revenue maximization, and the edge cloud revenue under the elasticity pricing scheme is 24% better than that of the consistency pricing scheme.

Keywords

• |mobile edge computing (mec)|resource pricing mechanism|stackelberg game|reinforcement learning