Multi-Layer Decomposition of Network Utility Maximization Problems
Multi-Layer Decomposition of Network Utility Maximization Problems
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DOI：
10.1109/tnet.2020.3003925
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发表时间：
2020-07
期刊：
影响因子：
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通讯作者：
Nurullah Karakoç;A. Scaglione;A. Nedić;M. Reisslein
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文献类型：
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作者：
Nurullah Karakoç;A. Scaglione;A. Nedić;M. Reisslein
我们描述了一个分布式框架，用于解决通信、微观经济学和各种网络应用中出现的资源共享问题。特别是，我们考虑网络效用最大化的分层多层分解（ML-NUM），其中功能分配给不同的层。所提出的方法为资源分配问题创建了集中管理和分布式计算的解决方案。在非静态环境中，该技术旨在通过将通信和计算负担部分转移到网络边缘来减少延迟，从而快速响应网络的动态。我们的主要贡献是在假设网络变化与用于本地计算的算法的收敛时间相同的时间尺度下进行详细分析。此外，假设用户目标函数具有强凹性和平滑性，并且在每层的一些稳定性条件下，我们提出了 ML-NUM 框架的收敛速度和最优性界限。此外，通过数值例子证明了所提出方法的主要优点。
We describe a distributed framework for resource sharing problems that arise in communications, micro-economics, and various networking applications. In particular, we consider a hierarchical multi-layer decomposition for network utility maximization (ML-NUM), where functionalities are assigned to different layers. The proposed methodology creates solutions with central management and distributed computations to the resource allocation problems. In non-stationary environments, the technique aims to respond quickly to the dynamics of the network by decreasing delay by partially shifting the communication and computational burden to the network edges. Our main contribution is a detailed analysis under the assumption that the network changes are on the same time-scale as the convergence time of the algorithms used for local computations. Moreover, assuming strong concavity and smoothness of the users’ objective functions, and under some stability conditions for each layer, we present convergence rates and optimality bounds for the ML-NUM framework. In addition, the main benefits of the proposed method are demonstrated with numerical examples.