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：数据中心对现代社会的商业和社会活动至关重要，但同时也是主要的电力消耗者。为最小化其环境影响，必须在保持高服务质量（QoS）的同时提高数据中心的能效。基于这一考量，我们利用排队论开发了一个用于评估数据中心QoS的分析模型。此外，基于该模型，我们构建了一个领域特定的进化优化框架，该框架具备定制的解表示方法和约束感知初始化算子，用于在数据中心中寻找虚拟网络功能的最优放置方案，以优化能源消耗与QoS之间多个相互冲突的目标。特别地，我们的框架能以插件方式应用于任何现有的进化多目标优化算法。大量实验验证了我们所提出QoS模型的效率与准确性，以及针对不同规模虚拟网络功能放置问题的定制算法的有效性。