Towards energy-aware scheduling in data centers using machine learning

As energy-related costs have become a major economical factor for IT infrastructures and data-centers, companies and the research community are being challenged to find better and more efficient power-aware resource management strategies. There is a growing interest in “Green” IT and there is still a big gap in this area to be covered. In order to obtain an energy-efficient data center, we propose a framework that provides an intelligent consolidation methodology using different techniques such as turning on/off machines, power-aware consolidation algorithms, and machine learning techniques to deal with uncertain information while maximizing performance. For the machine learning approach, we use models learned from previous system behaviors in order to predict power consumption levels, CPU loads, and SLA timings, and improve scheduling decisions. Our framework is vertical, because it considers from watt consumption to workload features, and cross-disciplinary, as it uses a wide...

• Type: Conference
• Year: 2010
• Where: EENERGY
• Authors: Josep Lluis Berral, Iñigo Goiri, Ramon Nou, Ferran Julià, Jordi Guitart, Ricard Gavaldà, Jordi Torres
• Keywords: Computer Networks, Data Center, EENERGY 2010, Machine Learning, Machine Learning Techniques