Data centers' services restoration based on the decision-making of distributed agents

The increasing number of companies that are migrating their IT infrastructure to cloud environments has been motivated many studies on distributed backup strategies to improve the availability of these companies' systems. In this scenario, it is essential to study mechanisms to evaluate the network conditions to minimize the transmission time to improve the availability of the system. The goal of this study is to build models to evaluate the availability of services running in cloud data center infrastructure, emphasizing the impact of the variation of throughput on the data redundancy, and consequently, on the availability of the service. Based on it, this research purposes some smart models which can be deployed in each data center of a distributed arrange of data centers and help the system administrator to choose the best data center to restore the services of a faulty one. To analyze the impact of the network throughput over the service's availability, we gathered the MTTF and MTTR metrics of data center's components and services, generated a reliability block diagram to get the MTTF of the system as a whole, and developed a formalism to model the network component. Based on the results, we built an SPN model to represent the system and get the availability of it in many network conditions. After that, we analyze the availability of the system to discuss the impact of the network conditions over the system's availability. After building the models and get the system's availability in many network conditions, we can perceive the enormous impact of the network conditions over the system's availability through a plot that exhibits the annual downtime along of a year. Using the models developed to study the system availability, we developed smart agents capable of predicting the transfer time of a bulk of data and, with it, choose the data center with the best network conditions to restore the services of a faulty one.