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
Resource management for clusters of virtual machines
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
Autonomic virtual resource management for service hosting platforms
CLOUD '09 Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing
Communications of the ACM
Cloud Computing For Dummies
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
An energy-efficient self-provisioning approach for cloud resources management
ACM SIGOPS Operating Systems Review
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Due to all the pollutants generated by it and the steady increases in its rates, energy consumption has become a key issue. 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. Unsupervised learning in computers has for long been considered as the desired ambition of computer problems. Unlike conventional prediction-learning methods which assign credit by means of the difference between predicted and actual outcomes, the proposed study assigns credit by means of the difference between temporally successive predictions. We have shown that the proposed approach gives promising results.