Sabotage-tolerance mechanisms for volunteer computing systems
Future Generation Computer Systems - Best papers from symp. on cluster computing and the grid (CCGRID 2001)
Online Prediction of the Running Time of Tasks
Cluster Computing
The Vision of Autonomic Computing
Computer
Experiences with predicting resource performance on-line in computational grid settings
ACM SIGMETRICS Performance Evaluation Review
Proceedings of the 2nd conference on Computing frontiers
A Core Grid Ontology for the Semantic Grid
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
Mining for misconfigured machines in grid systems
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Future Generation Computer Systems
SCALEA-G: A unified monitoring and performance analysis system for the grid
Scientific Programming - AxGrids 2004
ASKALON: A Grid Application Development and Computing Environment
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Grid infrastructure monitoring system based on Nagios
Proceedings of the 2007 workshop on Grid monitoring
Proceedings of the 2007 workshop on Grid monitoring
gLite Job Provenance—a job-centric view
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
Eliciting honest value information in a batch-queue environment
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
Adaptive diagnosis in distributed systems
IEEE Transactions on Neural Networks
Self-adaptive change detection in streaming data with non-stationary distribution
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
Data stream clustering: A survey
ACM Computing Surveys (CSUR)
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The ever increasing scale and complexity of large computational systems ask for sophisticated management tools, paving the way toward Autonomic Computing. A first step toward Autonomic Grids is presented in this paper; the interactions between the grid middleware and the stream of computational queries are modeled using statistical learning. The approach is implemented and validated in the context of the EGEE grid. The GStrAP system, embedding the StrAP Data Streaming algorithm, provides manageable and understandable views of the computational workload based on gLite reporting services. An online monitoring module shows the instant distribution of the jobs in real-time and its dynamics, enabling anomaly detection. An offline monitoring module provides the administratorwith a consolidated view of the workload, enabling the visual inspection of its long-term trends.