Heterogeneous computing network resource management based on virtual network embedding

Abstract

With the booming development of new applications and demands led by large AI models, computing scale and technology are experiencing unprecedented rapid evolution and diversified innovation. However, as computing networks show a trend of clustering and heterogeneity, the contradiction between the rapid growth of computing demand and the inefficiency of resource utilization has become increasingly prominent. How to achieve unified and efficient management of heterogeneous computing to improve resource utilization has become an important research topic. Based on network virtualization (NV) technology, a heterogeneous cross-domain computing resource allocation method based on virtual network embedding (VNE) was proposed. Specifically, a policy network based on a deep reinforcement learning (DRL) model was constructed to accurately calculate candidate computing nodes and links for optimal resource allocation. Through a series of simulation experiments, it verifies the effectiveness of this method and provids new ideas for solving the problem of heterogeneous computing management.

Keywords

• heterogeneous computing network
• network virtualization
• virtual network embedding
• computing resource management
• deep reinforcement learning