WOWMOM '06 Proceedings of the 2006 International Symposium on on World of Wireless, Mobile and Multimedia Networks
Coordinating Multiple Autonomic Managers to Achieve Specified Power-Performance Tradeoffs
ICAC '07 Proceedings of the Fourth International Conference on Autonomic Computing
Tailoring Resources: The Energy Efficient Consolidation Strategy Goes Beyond Virtualization
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
An Evaluation of Server Consolidation Workloads for Multi-Core Designs
IISWC '07 Proceedings of the 2007 IEEE 10th International Symposium on Workload Characterization
Foundations of Green IT: Consolidation, Virtualization, Efficiency, and ROI in the Data Center
Foundations of Green IT: Consolidation, Virtualization, Efficiency, and ROI in the Data Center
Towards Thermal Aware Workload Scheduling in a Data Center
ISPAN '09 Proceedings of the 2009 10th International Symposium on Pervasive Systems, Algorithms, and Networks
Towards energy-aware scheduling in data centers using machine learning
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
A bio-inspired algorithm for energy optimization in a self-organizing data center
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
A Mathematical Programming Approach for Server Consolidation Problems in Virtualized Data Centers
IEEE Transactions on Services Computing
Server consolidation in Clouds through gossiping
WOWMOM '11 Proceedings of the 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks
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This paper proposes a swarm-inspired data center consolidation methodology which aims at reducing the power consumption in data centers while ensuring the workload execution within the pre-established performance parameters. Each data center server is managed by an intelligent agent that deals with its power efficiency by implementing a bird's migration-inspired behavior to decide on the appropriate server consolidation actions. The selected actions are executed to achieve an optimal utilization of server computing resources thus lowering power consumption. The data center servers self-organize in logical clusters according to the birds V-formation self-organizing migration model. The results are promising showing that the swarm-inspired data center consolidation methodology optimizes the utilization ratio of the data center computing resources and achieves estimated power savings of about 16%.