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Automated Decision Making Methods for the Multi-objective Optimization Task of Cloud Service Placement

机译：云服务布局多目标优化任务的自动化决策方法

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摘要

The network functions virtualization (NFV) paradigm provides advantages with respect to aspects like flexibility, costs, and scalability of networks. However, management and orchestration of the resulting networks also introduce new challenges. The placement of services and virtualized network functions (VNFs) is a multi-objective optimization task that confronts operators with a multitude of possible solutions that are incomparable among each other. The goal of this work is to investigate mechanisms that enable automated decision making between such multi dimensional solutions. To this end, we investigate techniques from the domain of multi attribute decision making that aggregate the performance of placements to a single numeric score. A comparison between resulting rankings of placements shows that many techniques produce similar results. Hence, placements that achieve good rankings according to many approaches might be viable candidates in the context of automated decision making.
机译：网络功能虚拟化（NFV）范式在网络的灵活性，成本和可伸缩性等方面具有优势。但是，由此产生的网络的管理和协调也带来了新的挑战。服务和虚拟网络功能（VNF）的放置是一个多目标优化任务，它使运营商面临着彼此之间无法比拟的众多可能解决方案。这项工作的目的是研究能够在此类多维解决方案之间进行自动决策的机制。为此，我们研究了来自多属性决策制定领域的技术，这些技术将展示位置的效果汇总到单个数字评分中。排名的结果排名之间的比较表明，许多技术都能产生相似的结果。因此，在自动决策的情况下，根据许多方法获得良好排名的展示位置可能是可行的候选对象。

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