Prophesy: an infrastructure for performance analysis and modeling of parallel and grid applications
ACM SIGMETRICS Performance Evaluation Review
Using Kernel Couplings to Predict Parallel Application Performance
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
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
Predicting the execution time of grid workflow applications through local learning
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Journal of Systems and Software
Efficient Autoscaling in the Cloud Using Predictive Models for Workload Forecasting
CLOUD '11 Proceedings of the 2011 IEEE 4th International Conference on Cloud Computing
On Performance Modeling and Prediction in Support of Scientific Workflow Optimization
SERVICES '11 Proceedings of the 2011 IEEE World Congress on Services
The panel of experts cloud pattern
Proceedings of the third international workshop on Cloud data management
Prediction-based auto-scaling of scientific workflows
Proceedings of the 9th International Workshop on Middleware for Grids, Clouds and e-Science
Achieving reproducibility by combining provenance with service and workflow versioning
Proceedings of the 6th workshop on Workflows in support of large-scale science
Predicting the Execution Time of Workflow Activities Based on Their Input Features
SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
Cloud computing for fast prediction of chemical activity
Future Generation Computer Systems
| Hi-index | 0.00 |
The ability to accurately predict the performance of software components executing within a Cloud environment is an area of intense interest to many researchers. The availability of an accurate prediction of the time taken for a piece of code to execute would be beneficial for both planning and cost optimisation purposes. To that end, this paper proposes a performance data capture and modelling architecture that can be used to generate models of code execution time that are dynamically updated as additional performance data is collected. To demonstrate the utility of this approach, the workflow engine within the e-Science Central Cloud platform has been instrumented to capture execution data with a view to generating predictive models of workflow performance. Models have been generated for both simple and more complex workflow components operating on local hardware and within a virtualised Cloud environment and the ability to generate accurate performance predictions given a number of caveats is demonstrated.