Runtime collaborative scheduling of resources in computing power network oriented to the wide-area distributed intelligent computing environment

With the rapid development of new generation information and communication technologies such as artificial intelligence, the wide-area distributed intelligent computing environment has become an important infrastructure. In response to the challenge of efficient collaborative scheduling of resources in a wide-area distributed intelligent computing environment, a runtime collaborative scheduling of resources in computing power network method is proposed. The key task selection and backfilling, network traffic scheduling of core flow, and adaptive data placement strategies are designed. The usage of computing, network, and storage resources in the computing power network are comprehensively analyzed, and the strategies are collaboratively implemented to optimized the global utilization of resources. The experimental results indicate that the proposed method can effectively improve the system throughput while optimizing the global data migration cost compared with the existing methods.