ACM Transactions on Computer Systems (TOCS)
Strongly Competitive Algorithms for Paging with Locality of Reference
SIAM Journal on Computing
Performance Analysis Using Stochastic Petri Nets
IEEE Transactions on Computers
Application level scheduling of gene sequence comparison on metacomputers
ICS '98 Proceedings of the 12th international conference on Supercomputing
Predicting parallel applications performance on non-dedicated cluster platforms
ICS '98 Proceedings of the 12th international conference on Supercomputing
Automatic node selection for high performance applications on networks
Proceedings of the seventh ACM SIGPLAN symposium on Principles and practice of parallel programming
Adaptive performance prediction for distributed data-intensive applications
SC '99 Proceedings of the 1999 ACM/IEEE conference on Supercomputing
Static performance prediction of data-dependent programs
Proceedings of the 2nd international workshop on Software and performance
A resource query interface for network-aware applications
Cluster Computing
Object Placement Using Performance Surfaces
Cluster Computing
Using Disk Throughput Data in Predictions of End-to-End Grid Data Transfers
GRID '02 Proceedings of the Third International Workshop on Grid Computing
Characterization and enhancement of Static Mapping Heuristics for Heterogeneous Systems
HiPC '00 Proceedings of the 7th International Conference on High Performance Computing
Predicting the Performance of Wide Area Data Transfers
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Characterizing NAS Benchmark Performance on Shared Heterogeneous Networks
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Performance Prediction of Data-Dependent Task Parallel Programs
Euro-Par '01 Proceedings of the 7th International Euro-Par Conference Manchester on Parallel Processing
Symbolic Performance Prediction of Data-Dependent Parallel Programs
TOOLS '02 Proceedings of the 12th International Conference on Computer Performance Evaluation, Modelling Techniques and Tools
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
Adaptive Distributed Applications on Heterogeneous Networks
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Predicting Sporadic Grid Data Transfers
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
The design of a performance steering system for component-based grid applications
Performance analysis and grid computing
Grid resource management
The impact of predictive inaccuracies on execution scheduling
Performance Evaluation - Performance modelling and evaluation of high-performance parallel and distributed systems
Using Regression Techniques to Predict Large Data Transfers
International Journal of High Performance Computing Applications
Performability modeling for scheduling and fault tolerance strategies for scientific workflows
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
Performance modeling of parallel applications for grid scheduling
Journal of Parallel and Distributed Computing
Load prediction using hybrid model for computational grid
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
WORKEM: Representing and Emulating Distributed Scientific Workflow Execution State
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Dynamic performance prediction of an adaptive mesh application
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Bandwidth variability prediction with rolling interval least squares (RILS)
Proceedings of the 50th Annual Southeast Regional Conference
Predictable quality of service atop degradable distributed systems
Cluster Computing
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Accurate performance predictions are difficult to achieve for parallel applications executing on production distributed systems. Conventional point-valued performance parameters and prediction models are ofen inaccurate since they can only represent one point in a range of possible behaviors. We address this problem by allowing characteristic application and system data to be represented by a set of possible values and their probabilities, which we call stochastic values.In this paper, we give a practical methodology for using stochastic values as parameters to adaptable performance prediction models. We demonstrate their usefulness for a distributed SOR application, showing stochastic values to be more effective than single (point) values in predicting the range of application behavior that can occur during execution in production environments.