Energy conservation in heterogeneous server clusters
Proceedings of the tenth ACM SIGPLAN symposium on Principles and practice of parallel programming
Resource Allocation Using Virtual Clusters
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
ICIT '09 Proceedings of the 2009 IEEE International Conference on Industrial Technology
Towards energy-aware scheduling in data centers using machine learning
Proceedings of the 1st International Conference on Energy-Efficient Computing and Networking
Performance and Power Management for Cloud Infrastructures
CLOUD '10 Proceedings of the 2010 IEEE 3rd International Conference on Cloud Computing
Power management for heterogeneous clusters: An experimental study
IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
An Energy Manager for High Performance Computer Clusters
ISPA '12 Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications
An overview of energy efficiency techniques in cluster computing systems
Cluster Computing
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This paper presents a general energy management system for High Performance Computing (HPC) clusters and cloud infrastructures that powers off cluster nodes when they are not being used, and conversely powers them on when they are needed. This system can be integrated with different HPC cluster middleware, such as Batch-Queuing Systems or Cloud Management Systems, and can also use different mechanisms for powering on and off the computing nodes. The presented system makes it possible to implement different energy-saving policies depending on the priorities and particularities of the cluster. It also provides a hook system to extend the functionality, and a sensor system in order to take into account environmental information. The paper describes the successful integration of the system proposed with some popular Batch-Queuing Systems, and also with some Cloud Management middlewares, presenting two real use-cases that show significant energy/costs savings of 27% and 17%.