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
Matchmaking: Distributed Resource Management for High Throughput Computing
HPDC '98 Proceedings of the 7th IEEE International Symposium on High Performance Distributed Computing
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
Analysis and modeling of job arrivals in a production grid
ACM SIGMETRICS Performance Evaluation Review
Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
IEEE Internet Computing
A survey of autonomic computing—degrees, models, and applications
ACM Computing Surveys (CSUR)
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
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
A Realistic Integrated Model of Parallel System Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Discovering Piecewise Linear Models of Grid Workload
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Identification, Modelling and Prediction of Non-periodic Bursts in Workloads
CCGRID '10 Proceedings of the 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing
Maximizing revenue in Grid markets using an economically enhanced resource manager
Concurrency and Computation: Practice & Experience - Economic Models and Algorithms for Grid Systems
COMPCHEM: Progress Towards GEMS a Grid Empowered Molecular Simulator and Beyond
Journal of Grid Computing
CCGRID '11 Proceedings of the 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Exact Bayesian curve fitting and signal segmentation
IEEE Transactions on Signal Processing
Self-Healing of Operational Workflow Incidents on Distributed Computing Infrastructures
CCGRID '12 Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)
Scalable structural break detection
Applied Soft Computing
Self-healing of workflow activity incidents on distributed computing infrastructures
Future Generation Computer Systems
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Despite intense research on Grid scheduling, differentiated quality of service remains an open question, and no consensus has emerged on the most promising strategy. The difficulties of experimentation might be one of the root causes of this stalling. An alternative to experimenting on real, large, and complex data is to look for well-founded and parsimonious representations, which may also contribute to the a-priori knowledge required for operational Autonomics. The goal of this paper is thus to explore explanatory and generative models rather than predictive ones. As a test case, we address the following issue: is it possible to exhibit and validate consistent models of the Grid workload? Most existing work on modeling the dynamics of Grid behavior describes Grids as complex systems, but assumes a steady-state system (technically stationarity) and concludes to some form of long-range dependence (slowly decaying correlation) in the associated time-series. But the physical (economic and sociologic) processes governing the Grid behavior dispel the stationarity hypothesis. This paper considers an appealing different class of models: a sequence of stationary processes separated by breakpoints. The model selection question is now defined as identifying the breakpoints and fitting the processes in each segment. Experimenting with data from the EGEE/EGI Grid, we found that a non-stationary model can consistently be identified from empirical data, and that limiting the range of models to piecewise affine (autoregressive) time series is sufficiently powerful. We propose and experiment a validation methodology that empirically addresses the current lack of theoretical results concerning the quality of the estimated model parameters. Finally, we present a bootstrapping strategy for building more robust models from the limited samples at hand.