Methodology for energy aware adaptive management of virtualized data centers
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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.
KeywordsEnergy efficient data centers Green management of computing resources Energy awareness Cloud computing Reinforcement learning
This work has been partially funded by the GAMES project (2012) and has been partly funded by the European Commission ICT activity of the 7th Framework Program (number ICT-248514). This work expresses the opinions of the authors and not necessarily those of the European Commission. The European Commission is not liable for any use that may be made of the information contained in this work. This document is a collaborative effort. The scientific contribution of all authors is the same.
Compliance with ethical standards
This study was funded by GAMES FP7 project (grant number ICT-248,514).
Conflict of interest
The authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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