IEEE Transactions on Computers
Adaptive algorithms for managing a distributed data processing workload
IBM Systems Journal
On maximizing service-level-agreement profits
Proceedings of the 3rd ACM conference on Electronic Commerce
On the quantification of e-business capacity
Proceedings of the 3rd ACM conference on Electronic Commerce
Layered Analytic Performance Modelling of a Distributed Database System
ICDCS '97 Proceedings of the 17th International Conference on Distributed Computing Systems (ICDCS '97)
Two-Level Iterative Queuing Modeling of Software Contention
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Statistical Service Assurances for Applications in Utility Grid Environments
MASCOTS '02 Proceedings of the 10th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunications Systems
Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
From monolithic to component-based performance evaluation of software architectures
Empirical Software Engineering
Future Generation Computer Systems
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Response time predictions for workload on new server architectures can enhance Service Level Agreement--based resource management. This paper evaluates two performance prediction methods using a distributed enterprise application benchmark. The historical method makes predictions by extrapolating from previously gathered performance data, while the layered queuing method makes predictions by solving layered queuing networks. The methods are evaluated in terms of: the systems that can be modelled; the metrics that can be predicted; the ease with which the models can be created and the level of expertise required; the overheads of recalibrating a model; and the delay when evaluating a prediction. The paper also investigates how a prediction-enhanced resource management algorithm can be tuned so as to compensate for predictive inaccuracy and balance the costs of SLA violations and server usage.