切片网络中基于测量的接纳控制：一种最佳支路识别方法
Measurement-based Admission Control in Sliced Networks: A Best Arm Identification Approach
摘要：
在切片网络中，切片的共享租赁需要基于网络资源的测量的数据流的自适应准入控制。在本文中，我们研究了基于测量的准入控制方案的设计，决定是否可以接纳一个新的数据流，在这种情况下，在哪片。目标是设计一种联合测量和决策策略，该策略返回正确的决策（例如，负载最小的切片），同时最小化测量成本（在做出决定之前进行的测量的数量）。我们研究了几个自然的录取标准，指定什么是正确的决定是这样的策略的设计。对于这些标准中的每一个，使用的工具，从最好的手臂识别土匪，我们首先推导出一个明确的信息理论的下限的成本，任何算法返回正确的决定与固定的信心。然后，我们设计了一个联合测量和决策策略，实现这一理论极限。我们比较经验，这些策略的测量成本，并比较它们的下限，以及一个天真的测量方案。我们发现，我们的算法显着优于天真的计划（一个因素$2-8$）。
英文摘要：
In sliced networks, the shared tenancy of slices requires adaptive admission control of data flows, based on measurements of network resources. In this paper, we investigate the design of measurement-based admission control schemes, deciding whether a new data flow can be admitted and in this case, on which slice. The objective is to devise a joint measurement and decision strategy that returns a correct decision (e.g., the least loaded slice) with a certain level of confidence while minimizing the measurement cost (the number of measurements made before committing to the decision). We study the design of such strategies for several natural admission criteria specifying what a correct decision is. For each of these criteria, using tools from best arm identification in bandits, we first derive an explicit information-theoretical lower bound on the cost of any algorithm returning the correct decision with fixed confidence. We then devise a joint measurement and decision strategy achieving this theoretical limit. We compare empirically the measurement costs of these strategies, and compare them both to the lower bounds as well as a naive measurement scheme. We find that our algorithm significantly outperforms the naive scheme (by a factor $2-8$).
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