Machine Learning
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Gathering at the well: creating communities for grid I/O
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Host load prediction using linear models
Cluster Computing
Using Queue Time Predictions for Processor Allocation
IPPS '97 Proceedings of the Job Scheduling Strategies for Parallel Processing
Using Run-Time Predictions to Estimate Queue Wait Times and Improve Scheduler Performance
IPPS/SPDP '99/JSSPP '99 Proceedings of the Job Scheduling Strategies for Parallel Processing
Proceedings of the 2003 ACM/IEEE conference on Supercomputing
Soft Benchmarks-Based Application Performance Prediction Using a Minimum Training Set
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
A reinforcement learning approach to dynamic resource allocation
Engineering Applications of Artificial Intelligence
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
Future Generation Computer Systems
Utility-Based Reinforcement Learning for Reactive Grids
ICAC '08 Proceedings of the 2008 International Conference on Autonomic Computing
Eliciting honest value information in a batch-queue environment
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
Trace-based evaluation of job runtime and queue wait time predictions in grids
Proceedings of the 18th ACM international symposium on High performance distributed computing
Injecting realistic burstiness to a traditional client-server benchmark
ICAC '09 Proceedings of the 6th international conference on Autonomic computing
Characterization of a computational grid as a complex system
GMAC '09 Proceedings of the 6th international conference industry session on Grids meets autonomic computing
Investigating autonomic behaviours in grid-basedcomputational science applications
GMAC '09 Proceedings of the 6th international conference industry session on Grids meets autonomic computing
GMAC '09 Proceedings of the 6th international conference industry session on Grids meets autonomic computing
Toward autonomic grids: analyzing the job flow with affinity streaming
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Best-effort parallel execution framework for Recognition and mining applications
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Maximizing revenue in Grid markets using an economically enhanced resource manager
Concurrency and Computation: Practice & Experience - Economic Models and Algorithms for Grid Systems
Towards Non-Stationary Grid Models
Journal of Grid Computing
Scalable structural break detection
Applied Soft Computing
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Despite extensive research focused on enabling QoS for grid users through economic and intelligent resource provisioning, no consensus has emerged on the most promising strategies. On top of intrinsically challenging problems, the complexity and size of data has so far drastically limited the number of comparative experiments. An alternative to experimenting on real, large, and complex data, is to look for well-founded and parsimonious representations. This study is based on exhaustive information about the gLite-monitored jobs from the EGEE grid, representative of a significant fraction of e-science computing activity in Europe. Our main contributions are twofold. First we found that workload models for this grid can consistently be discovered from the real data, and that limiting the range of models to piecewise linear time series models is sufficiently powerful. Second, we present a bootstrapping strategy for building more robust models from the limited samples at hand.