Data centers are critical to the commercial and social activities of modern society but are also major electricity consumers. To minimize their environmental impact, it is imperative to make data centers more energy efficient while maintaining a high quality of service (QoS). Bearing this consideration in mind, we develop an analytical model using queueing theory for evaluating the QoS of a data center. Furthermore, based on this model, we develop a domain-specific evolutionary optimization framework featuring a tailored solution representation and a constraint-aware initialization operator for finding the optimal placement of virtual network functions in a data center that optimizes multiple conflicting objectives with regard to energy consumption and QoS. In particular, our framework is applicable to any existing evolutionary multi-objective optimization algorithm in a plug-in manner. Extensive experiments validate the efficiency and accuracy of our QoS model as well as the effectiveness of our tailored algorithms for virtual network function placement problems at various scales.
翻译:数据中心对于现代社会的商业和社会活动至关重要,但也是重要的电力消费者。为了最大限度地减少对环境的影响,必须提高数据中心的能效,同时保持高质量的服务(Qos)。考虑到这一考虑,我们开发了一个分析模型,使用排队理论来评价数据中心的QOS。此外,基于这一模型,我们开发了一个针对特定领域的演化优化框架,其中包含一个量身定制的解决方案代表和一个有限制的初始化操作器,以找到将虚拟网络功能最佳地安置在一个数据中心,从而优化能源消费和Qos方面的多重相互矛盾的目标。特别是,我们的框架适用于任何现有的演进多目标优化算法,以插插座方式。广泛的实验验证了我们的Qos模型的效率和准确性,以及我们针对虚拟网络功能在不同规模的定位问题的定制算法的有效性。