A Recurrent Self-Organizing Map for Temporal Sequence Processing
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
Virtual Clusters for Grid Communities
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Autonomic virtual resource management for service hosting platforms
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Towards autonomic computing systems
Engineering Applications of Artificial Intelligence
Communications of the ACM
Statistical machine learning makes automatic control practical for internet datacenters
HotCloud'09 Proceedings of the 2009 conference on Hot topics in cloud computing
Adaptive resource provisioning for read intensive multi-tier applications in the cloud
Future Generation Computer Systems
Unsupervised Neural Predictor to Auto-administrate the Cloud Infrastructure
UCC '12 Proceedings of the 2012 IEEE/ACM Fifth International Conference on Utility and Cloud Computing
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In recent years, energy conservation has become a major issue in information technology. Cloud computing is an emerging model for distributed utility computing and is being considered as an attractive opportunity for saving energy through central management of computational resources. Obviously, a substantial reduction in energy consumption can be made by powering down servers when they are not in use. This work presents a resources provisioning approach based on an unsupervised predictor model in the form of an unsupervised, recurrent neural network based on a self-organizing map. Another unique feature of our work is a resources administration strategy for energy saving in the cloud. Such a strategy is implemented as a selfadministration module. We show that the proposed approach gives promising results.