Centralized Network Utility Maximization With Accelerated Gradient Method

Network utility maximization (NUM) is a fundamental problem for network traffic management and resource allocation. Due to the inherent decentralization and complexity of networks, much of the existing research has focused on developing decentralized algorithms for NUM. However, with the rise of Software-Defined Networking (SDN), especially in cloud networks and inter-datacenter networks managed by large enterprises, there has been growing interest in centralized NUM algorithms. To cope with the large and increasing number of flows in such SDN networks, existing studies on centralized NUM focus on the scalability of the algorithm with respect to the number of flows, but the efficiency is ignored. In this paper, we propose a centralized, efficient and scalable algorithm for the NUM problem. By designing smooth utility and penalty functions, we formulate the NUM problem with a smooth objective function, which enables the use of Nesterov’s accelerated gradient method (AGM). We prove that the proposed method achieves an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(d/t^{2})$</tex-math> </inline-formula> convergence rate, demonstrating superior convergence speed with respect to the number of iterations <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i>, and our method is scalable with respect to the number of flows <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> in the network. Our smooth objective NUM formulation and AGM are effective not only in simple network scenarios with non-prioritized flows routed on one simple paths, but also in more complex and practical scenarios involving prioritized flows routed across multiple complex paths. Experiment results confirm that our method obtains accurate solutions with fewer iterations, and achieves close-to-optimal network utility.

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Publication Details

Journal
IEEE Transactions on Networking
Published
2025-05-02
DOI
https://doi.org/10.1109/ton.2025.3562283
Primary Topic
Advanced MIMO Systems Optimization
Type
article
Field-Weighted Citation Impact
0.00

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article

Centralized Network Utility Maximization With Accelerated Gradient Method

Jiahai Yang, Xingang Shi, Ying Tian, Zhiliang Wang et al.
IEEE Transactions on Networking
Advanced MIMO Systems Optimization
article

Centralized Network Utility Maximization With Accelerated Gradient Method

Jiahai Yang, Xingang Shi, Ying Tian, Zhiliang Wang, Han Zhang, Xia Yin
article en

Abstract

Network utility maximization (NUM) is a fundamental problem for network traffic management and resource allocation. Due to the inherent decentralization and complexity of networks, much of the existing research has focused on developing decentralized algorithms for NUM. However, with the rise of Software-Defined Networking (SDN), especially in cloud networks and inter-datacenter networks managed by large enterprises, there has been growing interest in centralized NUM algorithms. To cope with the large and increasing number of flows in such SDN networks, existing studies on centralized NUM focus on the scalability of the algorithm with respect to the number of flows, but the efficiency is ignored. In this paper, we propose a centralized, efficient and scalable algorithm for the NUM problem. By designing smooth utility and penalty functions, we formulate the NUM problem with a smooth objective function, which enables the use of Nesterov’s accelerated gradient method (AGM). We prove that the proposed method achieves an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(d/t^{2})$</tex-math> </inline-formula> convergence rate, demonstrating superior convergence speed with respect to the number of iterations <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i>, and our method is scalable with respect to the number of flows <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</i> in the network. Our smooth objective NUM formulation and AGM are effective not only in simple network scenarios with non-prioritized flows routed on one simple paths, but also in more complex and practical scenarios involving prioritized flows routed across multiple complex paths. Experiment results confirm that our method obtains accurate solutions with fewer iterations, and achieves close-to-optimal network utility.

IEEE Transactions on NetworkingVol. 33(5)
National Natural Science Foundation of China
Openalex Percentile: Top 93%
Advanced MIMO Systems Optimization
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