Adaptive performance prediction for distributed data-intensive applications
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Capacity Planning for Web Services: metrics, models, and methods
Capacity Planning for Web Services: metrics, models, and methods
Semantically-Aided Data-Aware Service Workflow Composition
SOFSEM '09 Proceedings of the 35th Conference on Current Trends in Theory and Practice of Computer Science
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This article proposes an approach for predicting runtime of web services (WS) with state – also called stateful web services. Estimating WS runtime is particularly critical during construction of composite WS workflows. Each workflow job must be scheduled in a way that the overall workflow run time will satisfy the overall workflow constrains. Such workflows are commonly used in Grids for connecting individual Grid WS to large, complicated and distributed applications. Prediction of WS run times optimizes scheduling and supports efficient use of grid resources. In our approach we propose to estimate expected WS run time based on invocation parameters of WS operations, states of resources maintained by a WS and properties of resources used as processing inputs for a WS. We adopt knowledge based approach where the history of WS operations is examined and a model is created and updated for each class and instance of a WS. Such WS run time prediction models can be then used by workflow schedulers to compute expected run times of a range of WS for the purpose of identifying the most appropriate WS for a given job within given constrains.