Abstract
In this article, we consider an online version of the well-studied network utility maximization problem, where users arrive one by one and a network operator makes irrevocable rate allocation decisions for each user without knowing the details of future arrivals. We propose a threshold-based algorithm and analyze its worst-case performance. We prove that the competitive ratio of the proposed algorithm is logarithmic in the maximum number of links requested by a user. Extensive simulations are conducted to demonstrate the performance advantage of our proposed algorithm in comparison with two state-of-the-art algorithms. In addition, we devise an adaptive implementation of our algorithm with online learning.
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