Future Generation Computer Systems - Special issue on metacomputing
Approximate String Matching and Local Similarity
CPM '94 Proceedings of the 5th Annual Symposium on Combinatorial Pattern Matching
Explaining World Wide Web Traffic Self-Similarity
Explaining World Wide Web Traffic Self-Similarity
Ibis: a flexible and efficient Java-based Grid programming environment: Research Articles
Concurrency and Computation: Practice & Experience - 2002 ACM Java Grande–ISCOPE Conference Part II
Adjusted fair scheduling and non-linear workload prediction for QoS guarantees in grid computing
Computer Communications
KOALA: a co-allocating grid scheduler
Concurrency and Computation: Practice & Experience
Prediction-based real-time resource provisioning for massively multiplayer online games
Future Generation Computer Systems
Service specification in cloud environments based on extensions to open standards
Proceedings of the Fourth International ICST Conference on COMmunication System softWAre and middlewaRE
On grid performance evaluation using synthetic workloads
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
Performance Analysis of Cloud Computing Services for Many-Tasks Scientific Computing
IEEE Transactions on Parallel and Distributed Systems
Quality architecture for resource allocation in cloud computing
ESOCC'12 Proceedings of the First European conference on Service-Oriented and Cloud Computing
Self-adaptive workload classification and forecasting for proactive resource provisioning
Proceedings of the 4th ACM/SPEC International Conference on Performance Engineering
A Value Based Dynamic Resource Provisioning Model in Cloud
International Journal of Cloud Applications and Computing
A Value Based Dynamic Resource Provisioning Model in Cloud
International Journal of Cloud Applications and Computing
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The Cloud phenomenon brings along the cost-saving benefit of dynamic scaling. As a result, the question of efficient resource scaling arises. Prediction is necessary as the virtual resources that Cloud computing uses have a setup time that is not negligible. We propose an approach to the problem of workload prediction based on identifying similar past occurrences of the current short-term workload history. We present in detail the Cloud client resource auto-scaling algorithm that uses the above approach to help when scaling decisions are made, as well as experimental results by using real-world Cloud client application traces. We also present an overall evaluation of this approach, its potential and usefulness for enabling efficient auto-scaling of Cloud user resources.