Centralized Network Utility Maximization with Accelerated Gradient Method

TL;DR

This paper introduces a scalable, efficient centralized algorithm for network utility maximization in SDN networks, leveraging smooth optimization and accelerated gradient methods to improve convergence speed and solution accuracy.

Contribution

The paper develops a novel centralized NUM algorithm using smooth functions and Nesterov's acceleration, achieving the fastest convergence rate and scalability with respect to network size.

Findings

Achieves $O(d/t^2)$ convergence rate, fastest among similar methods.

Obtains accurate, near-optimal solutions with fewer iterations.

Demonstrates scalability with the number of network flows.

Abstract

Network utility maximization (NUM) is a well-studied problem for network traffic management and resource allocation. Because of the inherent decentralization and complexity of networks, most researches develop decentralized NUM algorithms. In recent years, the Software Defined Networking (SDN) architecture has been widely used, especially in cloud networks and inter-datacenter networks managed by large enterprises, promoting the design of centralized NUM algorithms. To cope with the large and increasing number of flows in such SDN networks, existing researches about centralized NUM focus on the scalability of the algorithm with respect to the number of flows, however the efficiency is ignored. In this paper, we focus on the SDN scenario, and derive a centralized, efficient and scalable algorithm for the NUM problem. By the designing of a smooth utility function and a smooth penalty function, we formulate the NUM problem with a smooth objective function, which enables the use of Nesterov's accelerated gradient method. We prove that the proposed method has $O(d/t^2)$ convergence rate, which is the fastest with respect to the number of iterations $t$, and our method is scalable with respect to the number of flows $d$ in the network. Experiments show that our method obtains accurate solutions with less iterations, and achieves close-to-optimal network utility.