Optimum VM Placement for NFV Infrastructures

T. Cucinotta, Luigi Pannocchi, Filippo Galli, S. Fichera, Sourav Lahiri, Antonino Artale
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引用次数: 2

Abstract

This paper constitutes an industrial experience re-port about the use of data center optimization strategies for softwarized network services within the Vodafone resource man-agement unit for the management of virtualized network infras-tructures. The problem of optimum virtual machine placement as needed in the network operator context is detailed, and different solving strategies are proposed and discussed, including heuristics based on genetic optimization. Also, experimental results are presented that compare these strategies with one another from the standpoint of optimality and execution times, using a data-set made of some of the real problems that had to be solved in the past few years by Vodafone, in order to optimize its capacity planning decisions. The presented experimental results highlight that an optimum solver leads to excessively high computation times for large problems, whereas simple heuristics may exhibit significant loss in optimality at reduced computation times. Genetic optimization, on the other hand, constitutes a very interesting trade-off between these two extremes. The data-set used for the provided results is published under an open data license, for possible reuse in future research works on the topic.
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NFV基础设施的最佳虚拟机布局
本文构成了一份关于在沃达丰资源管理单元内使用软件网络服务的数据中心优化策略来管理虚拟化网络基础设施的行业经验报告。详细讨论了网络运营商环境下虚拟机的最优布局问题,提出并讨论了不同的求解策略,包括基于遗传优化的启发式算法。此外,实验结果从最优性和执行时间的角度比较了这些策略,使用由沃达丰在过去几年中必须解决的一些实际问题组成的数据集,以优化其容量规划决策。实验结果表明,对于大型问题,最优解会导致过高的计算时间,而简单的启发式可能会在减少计算时间时表现出显著的最优性损失。另一方面,遗传优化构成了这两个极端之间的一种非常有趣的权衡。用于提供结果的数据集是在开放数据许可下发布的,以便在未来关于该主题的研究工作中可能重用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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