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On the performance of greedy algorithms in packet buffering

机译:贪婪算法在数据包缓冲中的性能

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We study a basic buffer management problem that arises in network switches. Consider m input ports, each of which is equipped with a buffer (queue) of limited capacity. Data packets arrive online and can be stored in the buffers if space permits; otherwise packet loss occurs. In each time step the switch can transmit one packet from one of the buffers to the output port. The goal is to maximize the number of transmitted packets. Simple arguments show that any reasonable algorithm, which serves any non-empty buffer, is 2-competitive. Azar and Richter recently presented a randomized online algorithm and gave lower bounds for deterministic and randomized strategies.In practice greedy algorithms are very important because they are fast, use little extra memory and reduce packet loss by always serving a longest queue. In this paper we first settle the competitive performance of the entire family of greedy strategies. We prove that greedy algorithms are not better than 2-competitive no matter how ties are broken. Our lower bound proof uses a new recursive construction for building adversarial buffer configurations that may be of independent interest. We also give improved lower bounds for deterministic and randomized online algorithms.In the second part of the paper we present the first deterministic online algorithm that is better than 2-competitive. We develop a modified greedy algorithm, called Semi-Greedy, and prove that it achieves a competitive ratio of $17/9 ≅ 1. 89$. The new algorithm is simple, fast and uses little extra memory. Only when the risk of packet loss is low, it does not serve the longest queue. Additionally we study scenarios when an online algorithm is granted additional resources. We consider resource augmentation with respect to memory and speed, i. e. an online algorithm may be given larger buffers or higher transmission rates. We analyze greedy and other online strategies.
机译:我们研究了网络交换机中出现的基本缓冲区管理问题。考虑m个输入端口,每个输入端口都配备了容量有限的缓冲区(队列)。数据包在线到达,并且在空间允许的情况下可以存储在缓冲区中;否则会发生丢包。在每个时间步中,交换机都可以将一个数据包从缓冲区之一发送到输出端口。目标是使传输的数据包数量最大化。简单的论据表明,任何可服务于任何非空缓冲区的合理算法都是2竞争性的。 Azar和Richter最近提出了一种随机在线算法,并为确定性和随机策略提供了下限。在实践中,贪婪算法非常重要,因为它们速度快,占用很少的额外内存并通过始终为最长的队列服务来减少数据包丢失。在本文中,我们首先确定整个贪婪策略家族的竞争绩效。我们证明,无论关系如何破裂,贪婪算法都不比2竞争性好。我们的下界证明使用新的递归构造来构建可能具有独立利益的对抗性缓冲区配置。我们还给出了确定性和随机性在线算法的改进下界。在本文的第二部分,我们提出了第一种优于2竞争性的确定性在线算法。我们开发了一种改进的贪婪算法,称为 Semi-Greedy ,并证明该算法达到了$ 17/9≅1. 89 $的竞争比。新算法简单,快速且几乎不占用额外的内存。仅当数据包丢失的风险较低时,它才不会服务于最长的队列。此外,我们研究了授予在线算法其他资源的情况。我们考虑关于内存和速度的资源增加,即。 e。可以为在线算法提供更大的缓冲区或更高的传输速率。我们分析贪婪和其他在线策略。

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