Matchmaking scientific workflows in grid environments

In this paper we analyze the scientific workflow matchmaking problem in Grid environments and combine workflow mapping and scheduling. Based on the characteristics of Grids, a new resource model is proposed. Motivated by the observations that not all jobs can run on all resources and that resource-critical jobs should be considered with their ancestor and descendant jobs when mapping, a novel resource-critical algorithm is designed based on a new Grid resource model. By means of experiments, it is shown to have good performance.

[1]  Ian T. Foster,et al.  Condor-G: A Computation Management Agent for Multi-Institutional Grids , 2004, Cluster Computing.

[2]  Rajkumar Buyya,et al.  Critical-path and priority based algorithms for scheduling workflows with parameter sweep tasks on global grids , 2005, 17th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD'05).

[3]  Rizos Sakellariou,et al.  Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources , 2007, Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07).

[4]  Rizos Sakellariou,et al.  A low-cost rescheduling policy for efficient mapping of workflows on grid systems , 2004, Sci. Program..

[5]  Ken Kennedy,et al.  Scheduling strategies for mapping application workflows onto the grid , 2005, HPDC-14. Proceedings. 14th IEEE International Symposium on High Performance Distributed Computing, 2005..

[6]  Richard Wolski,et al.  QBETS: queue bounds estimation from time series , 2007, SIGMETRICS '07.

[7]  Y.-K. Kwok,et al.  Static scheduling algorithms for allocating directed task graphs to multiprocessors , 1999, CSUR.

[8]  Richard Wolski,et al.  The network weather service: a distributed resource performance forecasting service for metacomputing , 1999, Future Gener. Comput. Syst..

[9]  R. Buyya,et al.  A budget constrained scheduling of workflow applications on utility Grids using genetic algorithms , 2006, 2006 Workshop on Workflows in Support of Large-Scale Science.

[10]  Salim Hariri,et al.  Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing , 2002, IEEE Trans. Parallel Distributed Syst..

[11]  Geoffrey C. Fox,et al.  The Open Grid Computing Environments collaboration: portlets and services for science gateways , 2007, Concurr. Comput. Pract. Exp..

[12]  Rizos Sakellariou,et al.  An Experimental Investigation into the Rank Function of the Heterogeneous Earliest Finish Time Scheduling Algorithm , 2003, Euro-Par.

[13]  ProdanRadu,et al.  Scheduling of scientific workflows in the ASKALON grid environment , 2005 .

[14]  Charles E. Catlett.  TeraGrid: A Foundation for US Cyberinfrastructure , 2005, NPC.

[15]  Ishfaq Ahmad,et al.  Dynamic Critical-Path Scheduling: An Effective Technique for Allocating Task Graphs to Multiprocessors , 1996, IEEE Trans. Parallel Distributed Syst..

[16]  Douglas Thain,et al.  Distributed computing in practice: the Condor experience , 2005, Concurr. Pract. Exp..

[17]  Ken Kennedy,et al.  TaskScheduling Strategies forWorkflow-based Applications inGrids , 2005 .

[18]  Rizos Sakellariou,et al.  Advance Reservation Policies for Workflows , 2006, JSSPP.

[19]  Radu Prodan,et al.  Scheduling of scientific workflows in the ASKALON grid environment , 2005, SGMD.

[20]  Geoffrey C. Fox,et al.  Smart Mining of Drug Discovery Information : 1 . A web service and workflow infrastructure , 2007 .

[21]  Ian T. Foster.  Globus Toolkit Version 4: Software for Service-Oriented Systems , 2005, NPC.

[22]  Rizos Sakellariou,et al.  A hybrid heuristic for DAG scheduling on heterogeneous systems , 2004, 18th International Parallel and Distributed Processing Symposium, 2004. Proceedings..