Emotion-Driven System for Data Center Management

Abstract

Complex Information Systems and infrastructures, like Smart Cities, must be efficiently operated, minimizing inefficiencies and maximizing productivity. Traditional approaches are focused on improving the systems, automating processes and services, leaving aside human and emotions considerations. To achieve this efficient operation, we attempted to cover both sides. We found new ways to capture the information coming from the workforce, in our case, the operations management team, and, merged this information with the data from the IoT sensors from the systems, enabling a holistic view of the entire operations occurring in real-time. In a Data Center environment, we have developed a set of tools for capturing the emotional data in order to detect potential biases caused by the specific mood of the person inside the operations team. We used Artificial Intelligence algorithms for finding the patterns that will help us to manage the system in the future. We compared and verified our findings with the existing references from other disciplines, e.g., Psychology. In this article, we expose some methods to be developed in future studies for supervising and increasing productivity in Data Centers, as a useful example for Smart Cities. Our research focuses on monitoring the mood and the emotional status of the personnel responsible for operating the system. We use this emotional data as an input for measurement.

Keywords

• smart cities
• internet of things
• artificial intelligence
• management
• data fusion
• buildings and infrastructures
• data centers
• control platforms
• policy development
• sensors and actuators