## 2 Related Works  
This section discusses the research works that implement improvements to save energy in a data center context. [PERSON] et al. [41], after comparing strategies to find subsets of minimum power, built a software manager named ElasticTree, a system to dynamically adapt the energy consumption of a network of the data center, which consists of three logic modules: optimizer, routing, and power control. Initially, a table test is conducted on a data traffic test basis built with Open Flow switches [42]. Subsequently, they conducted accurate tests by monitoring traffic on electronic sites. In both tests, the authors assessed the trade-offs between energy efficiency, performance, and robustness. The results demonstrated energy savings of up to 50%. [PERSON] et al. [43] propose SAVE, a Software Defined Networks (SDN)-based solution for incorporating a Virtual Data Center (VDC) with assisted energy efficiency. The proposed solution is based on the scheme that finds ideal VDC component mappings and ideal routing paths in various data center environments to reduce energy consumption, which was 18.75%. [PERSON] et al. [28] investigated whether airflow management and the data center location selection can affect energy consumption. In the study, the author presents a rack layout with a vertical cooling airflow. They explore two cooling systems, a computer room air conditioning cooling system and an air saver. Based on these two cooling systems, four cities were selected from data center locations worldwide. Furthermore, they implement energy efficiency metrics to cool the data center, such as energy efficiency, performance coefficient, and chiller hours. The results show that refrigeration efficiency and operating costs vary significantly with different climatic conditions, energy prices, and refrigeration technologies. The authors conclude that climatic conditions are the main factor affecting the air saver, and the maximum energy saving indicated in the study was 35%. In [44], [PERSON] et al. investigated the problem of incorporating virtual data centers aware of energy use. They propose a model of energy consumption, including the virtual machine node models and the virtual switch node, to measure energy consumption when incorporating a virtual data center quantitatively. In addition, using this model, they implement a heuristic algorithm and another based on particle swarm optimization. The second algorithm proved to be the best solution for the incorporation of the virtual data center. The results of the experiment show an energy saving of 11% up to 28%. [PERSON] et al. [45] explored deep reinforcement learning coupled with SDN to improve the energy efficiency of data center networks and ensure flow completion time. The proposed solution dynamically collects the traffic distribution of the switches to train the model. The trained model can quickly analyze complicated traffic characteristics using neural networks, produce an adaptive action to schedule flows, and deliberately configure margins for different links. After the action, the flows are consolidated into some active links and switches to save energy. The refine margin setting for active links avoids the violation of the flow completion time. According to the authors, the simulation results demonstrate energy savings of up to 12.2 % compared to the existing approaches in the experiment's data center, such as the ElasticTree proposed by [PERSON] et al. [41]. As the authors do not present the actual values, which compromises the comparison with other works in the literature, it is impossible to obtain a realistic estimate of the energy savings gain. Furthermore, the data center and the applications are different. [PERSON] et al. [46] have proposed a model optimized for energy efficiency to reduce energy consumption and complete more tasks with greater efficiency in virtual machines in an environment of a cloud. The authors take advantage of the performance/power ratio to define upper limits for overload detection.