Profit-Aware Computing Server Clustering and Task Scheduling in the Computing Power Network

Computing power network has emerged as an attractive technology to tackle the increasing demand for computational resources in cloud networking. In this paper, we study the problem of optimizing the formation of clusters of computing servers/nodes and also the task scheduling considering the games between the platform and computing nodes to maximize the platform profit. We formulate this problem as an integer programming problem. We propose a deep reinforcement-learning based server clustering and auction-based task scheduling algorithm working at different time scales to solve the problem. The deep reinforcement learning-based server clustering algorithm works at a large time scale to optimize the sizes and compositions of different clusters based on temporal and spatial distribution of the tasks and also their characteristics. The auction-based task scheduling algorithm works at a small time scale to match the tasks with the clusters while satisfying the QoS requirements of tasks so as to maximize the profit of the platform. Extensive simulations are conducted to evaluate the performance of proposed algorithm and the results show its high performance.