  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.
