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

机译：云服务放置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）范例对于相对于灵活性，成本和网络的可扩展性等方面提供了优势。但是，由此产生的网络的管理和编排也引入了新的挑战。服务和虚拟化网络功能的放置（VNFS）是一种多目标优化任务，其面对具有彼此无与伦比的可能的解决方案的运算符。这项工作的目标是调查在这种多维解决方案之间实现自动决策的机制。为此，我们调查来自多个属性决策域的技术，将展示要素的性能聚合到单个数字分数。所得到的放置排名之间的比较表明，许多技术产生了类似的结果。因此，根据许多方法实现良好排名的展示在自动决策中可能是可行的候选者。

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