Task scheduling optimization and fairness index design in collaborative AI computing platform

The platform faces the dual challenges of efficiency and fairness in task scheduling. The traditional efficiency-oriented algorithm easily leads to the monopoly of "the strong will always be strong" resources, which leads to the long-term idleness of weak nodes and the withdrawal of participants. However, the classical fairness theory is difficult to adapt to the heterogeneous requirements of AI tasks for computing, communication and energy consumption. Therefore, this paper proposes a dynamic task scheduling optimization framework that integrates the three-dimensional fairness index of "time domain-space domain-result". Firstly, a compound fairness quantitative model based on Gini coefficient is designed to measure the consistency of task waiting opportunity, node load and completion quality respectively, so as to solve the one-sided problem of single index; Secondly, a dynamic weight scheduling algorithm driven by reinforcement learning (RL) is constructed, and the efficiency and fairness weights are adaptively adjusted through system state feedback to realize real-time optimization of resource allocation. Experimental results show that, in the high-load simulation environment, the algorithm improves the compound fairness by 32% without losing the system throughput, which is significantly better than the traditional FIFO, load balancing and fixed weight RL strategy. The research provides theoretical support and technical path for the sustainable resource scheduling of collaborative AI platform.