## 1 Introduction  
The evolution of computational resources has allowed the emergence of a set of applications in the most diverse platforms, such as web, desktop, mobile, and application contexts, such as finance management [1, 2], privacy and safety [3], health [4, 5], education [6], smart cities [7, 8], smart homes [9], and smart things [10]. Things that could not be processed on a personal computer 20 years ago can currently be processed by a micro-chip available in a smartwatch or an appliance. Given the large set of applications, data sources, and online services, the demand for data processing and storage resources has been increasing, and there is a lack of large data centers. According to the forecast demonstrated in [11], between the years 2019 and 2025, there will be an annual increase of 2% in the global data center market, indicating (i) the increase in the construction of new data centers in super scale; (ii) growth in the acquisition of flash technology for critical applications; (iii) the adoption of alternative forms of energy; (iv) the use of 200 and 400 Gb switch ports, and (v) the increase in the use of hyper-convergent and convergent infrastructure. In [11], the authors point out that more than 100 data centers have been implemented with an energy capacity of more than 15 MW in recent years. According to the study, one of the reasons for the increase is related to the fact that telecommunications providers are investing in optimizing broadband in several locations. Another indication is that government agencies drive initiatives to develop smart cities and the digital economy's growth. The growing increase in data produced by applications has taken us to a level where knowledge management and massive data volume are no longer non-functional softwareand hardware architecture requirements. It started and was treated as a functional and essential requirement. This is particularly noteworthy with the emergence of sophisticated paradigms and innovative approaches to data organization, control, and processing, such as the publish-subscribe [12; 13] and fog-computing [14] and the Apache Hadoop frameworks [15; 16; 17], Spark [18; 19; 20], Kafka [21; 22], Perforce and Git [23]. Likewise, several works and architectures seek to optimize the use of computational resources and mitigate energy consumption in data centers, either by conducting changes in the local physical structure or via software-level optimizations. As it is widely known for the state-of-the-art and industry practice, in an organization, the data center is the sector responsible for storing, processing, scaling, and managing data traffic from all sectors and applications [24; 25; 26]. Among the equipment involved, there is equipment that acts directly and indirectly in the processing and storage of data, such as, e.g., servers, switches, routers, and mass storage devices. It involves managing critical, confidential, and essential information for the business, ranging from sensitive customer data to the institution's strategic planning data. Since it is directly aligned with the organization's profitable business, the requirement for providing a service with high reliability, security, responsiveness, and real-time support is increased. Keeping each piece of equipment and components intact is essential. An untreated failure in an institution's data center can range from a pause in receiving e-mails to a complicated and risky process of recovering corrupted data. In short, this must involve mechanisms and strategies for self-organization, replication, and redundancy, processes recognized for guaranteeing the requirements demanded, and increasing computational and energy consumption. Likewise, the need for high availability of services and the continuous use of equipment 24 h a day, 7 days a week, also caused increases in heat generated during data processing and provision of all these services involved.