Methodology for energy aware adaptive management of virtualized data centers

This paper proposes a methodology for energy aware management of virtualized data centers (DC) based on dynamically adapting and scaling the computing capacity to the characteristics of the workload. To assess the energy efficiency of DC operation, we have defined a novel ontological model for representing its energy and performance characteristics and a new metric for aggregating Green and Key Performance Indicators and calculating at run-time the DC Greenness Level. Workload balancing and consolidation is achieved by means of an automated reinforcement learning-based decision process targeting to increase the workload density and to scale down the unused computing resources. Evaluation results show that up to 15.6 % energy savings are obtained on our test bed DC. Tests conducted in a simulated environment show that the time and space overhead of our methodology are within reasonable limits and that by organizing the servers in hierarchical clusters, the methodology can manage highly dynamic workload in large DCs with thousands of servers. The methodology is already implemented in the Green Cloud Scheduler, an official component of the OpenNebula Middleware which is available in the OpenNebula Ecosystem web site to be downloaded and used.