ACSW Frontiers '05 Proceedings of the 2005 Australasian workshop on Grid computing and e-research - Volume 44
Workflow task clustering for best effort systems with Pegasus
Proceedings of the 15th ACM Mardi Gras conference: From lightweight mash-ups to lambda grids: Understanding the spectrum of distributed computing requirements, applications, tools, infrastructures, interoperability, and the incremental adoption of key capabilities
Flexible and Efficient Workflow Deployment of Data-Intensive Applications On Grids With MOTEUR
International Journal of High Performance Computing Applications
Grouping-Based Fine-Grained Job Scheduling in Grid Computing
ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 01
P-GRADE portal family for grid infrastructures
Concurrency and Computation: Practice & Experience
On-Line task granularity adaptation for dynamic grid applications
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
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)
Task granularity policies for deploying bag-of-task applications on global grids
Future Generation Computer Systems
MRI estimation of T1 relaxation time using a constrained optimization algorithm
MBIA'12 Proceedings of the Second international conference on Multimodal Brain Image Analysis
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Controlling the granularity of workflow activities executed on widely distributed computing platforms such as grids is required to reduce the impact of task queuing and data transfer time. Most existing granularity control approaches assume extensive knowledge about the applications and resources (e.g. task duration on each resource), and that both the workload and available resources do not change over time. We propose a granularity control algorithm for platforms where such clairvoyant and offline conditions are not realistic. Our method groups tasks when the fineness degree of the application, which takes into account the ratio of shared data and the queuing/round-trip time ratio, becomes higher than a threshold determined from execution traces. The algorithm also de-groups task groups when new resources arrive. The application's behavior is constantly monitored so that the characteristics useful for the optimization are progressively discovered. Experimental results, obtained with 3 workflow activities deployed on the European Grid Infrastructure, show that (i) the grouping process yields speed-ups of about 2.5 when the amount of available resources is constant and that (ii) the use of de-grouping yields speed-ups of 2 when resources progressively appear.