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arXiv 1309.7367cs.NIcs.LGmath.OC
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随机在线最短路径路由:反馈的价值

Stochastic Online Shortest Path Routing: The Value of Feedback

KTH Royal Institute of Technology(瑞典皇家理工学院);Ericsson Research(爱立信研究院);Supelec(法国高等电力学院)

M. Sadegh Talebi, Zhenhua Zou, Richard Combes, Alexandre Proutiere, Mikael Johansson

摘要:

研究了多跳网络中的在线最短路径路由问题。链路成本或延迟是时变的,并且由独立同分布的随机过程建模,其参数最初是未知的。这些参数以及最优路径只能通过在网络中路由数据包并观察实现的延迟来估计。我们的目标是找到一个路由策略,最大限度地减少遗憾(预期延迟的累积差异)之间的路径选择的政策和未知的最优path.We制定的问题作为一个组合的强盗优化问题,并考虑几种情况下,不同的路由决策和决策时提供的信息。对于每一种情况下,我们得出一个严格的渐近下限的遗憾,必须满足任何在线路由策略。这些界限有助于我们理解当(i)在每一跳而不是仅在源处进行路由决策,以及(ii)观察每个链路的延迟而不是端到端的路径延迟时,我们可以预期的性能改进。特别是,我们表明,(i)是没有用的,而(ii)可以有一个壮观的影响。三个算法,与计算复杂性和性能之间的权衡,提出。这些算法的遗憾上界改善了现有的算法,他们显着优于国家的最先进的算法在数值实验中。

英文摘要:

This paper studies online shortest path routing over multi-hop networks. Link costs or delays are time-varying and modeled by independent and identically distributed random processes, whose parameters are initially unknown. The parameters, and hence the optimal path, can only be estimated by routing packets through the network and observing the realized delays. Our aim is to find a routing policy that minimizes the regret (the cumulative difference of expected delay) between the path chosen by the policy and the unknown optimal path. We formulate the problem as a combinatorial bandit optimization problem and consider several scenarios that differ in where routing decisions are made and in the information available when making the decisions. For each scenario, we derive a tight asymptotic lower bound on the regret that has to be satisfied by any online routing policy. These bounds help us to understand the performance improvements we can expect when (i) taking routing decisions at each hop rather than at the source only, and (ii) observing per-link delays rather than end-to-end path delays. In particular, we show that (i) is of no use while (ii) can have a spectacular impact. Three algorithms, with a trade-off between computational complexity and performance, are proposed. The regret upper bounds of these algorithms improve over those of the existing algorithms, and they significantly outperform state-of-the-art algorithms in numerical experiments.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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