Asynchronous Distributed Integer Programming for Resource Allocation in Large-Scale Datacenter Networks

This paper tackles the challenge of resource allocation in large-scale datacenter networks (DCNs) using integer programming. The complexity of integer resource allocation problems (IRAP) lies in the non-convex nature of integer constraints, which makes achieving global optimality computationally challenging, especially in distributed and dynamic environments like DCNs. Traditional centralized methods struggle with scalability, efficiency, and adaptability to fluctuating workloads, which are common in data-intensive applications such as artificial intelligence (AI) and the Internet of Things (IoT). To address these challenges, we propose an asynchronous distributed resource allocation algorithm leveraging a gossip-based communication protocol. By transforming the resource allocation problem into an IRAP and using incremental cost functions, our method enables independent servers to make local decisions while converging to the global optimal solution. The algorithm is designed to overcome the difficulties of integer optimization in distributed networks, ensuring convergence through theoretical guarantees based on Markov processes. Simulations demonstrate the algorithm’s robustness, scalability, and effectiveness in various network scenarios.