Evolutionary Game Theory-Based Optimal Scheduling Strategy for Heterogeneous Computing

With the development of intelligent applications, simply relying on traditional single type of computing unit cannot efficiently satisfy diverse cloud requirements. The emergence of heterogeneous computing can efficiently achieve the adaptation of these intelligent applications by using different types of processing units such as Graphics Processing Unit (GPU) and Field Programmable Gate Array (FPGA). However, the trade-off between profit and costs in the process of scheduling heterogeneous computing resources is also an issue worthy of attention. To address this challenge, this work establishes a heterogeneous computing resource scheduling model based on Stackelberg differential game, which includes three roles Computing Power Trading Platforms (CPTPs), Heterogeneous Computing Service Providers (HCSPs), and Heterogeneous Computing Application Providers (HCAPs). The objective is to maximize utility function of CPTPs and HCSPs subject to rental ratio, pricing strategy and energy consumption of resource scheduling, which has proved that there exists a Stackelberg Nash Equilibrium (NE) solution. The Support Vector Machine based on Artificial Fish (SVM-AF) is proposed to predict the access times of heterogeneous computing applications. In addition, the distributed iteration method and Cauchy distribution is adopted to optimize the computing price strategy and improve its convergence performance. The simulation results show that compared with other strategies, the proposed strategy can effectively improve computing revenue of user experience and while reducing energy consumption in the process of resource scheduling.