Resource matching algorithm based on multidimensional computing resource measurement in computing power network

With the deep integration of computing and network development, as a new type of network infrastructure, computing power network (CPN) has become a research hotspot in the industry. Computing resource metrics integrates the computing resources connected to the CPN, realizes the collaborative management of heterogeneous resources through the measurement of multi-dimensional computing resource, and provides an accurate resource view for resource matching, which has become an important part of the CPN. The traditional measurement methods are too single to measure computing resources from a single dimension, which is difficult to adapt to the development of CPN. The existing methods of computing resource metrics need to be improved in the accuracy of resource matching and cannot reflect the comprehensive performance of computing resources. In this paper, a multi-dimensional computing resource measurement method based on entropy weight TOPSIS is designed to score the comprehensive performance of computing resources, storage resources and communication resources of computing nodes, then the nodes are divided into different categories of comprehensive performance according to the score, so as to narrow the scope of resource matching for different user requirements. At the same time, a multi-dimensional resource matching algorithm based on deep reinforcement learning is proposed. The resource matching process is constructed as a Markov decision process to realize the matching of tasks and nodes. The simulation results show that the proposed algorithm can better solve the matching problem of multi-dimensional resources, and the utilization rate of all kinds of resources reaches more than 90%.