Actas JP2011 - Universidad de La Laguna

Actas XXII Jornadas de Paralelismo (JP2011) , La Laguna, Tenerife, 7-9 septiembre 2011parámetros de entrada y adaptarse al cambio producido.Es por este motivo que nuestro trabajotrata de reducir la sobrecarga de la política dinámicagarantizando un buen makespan del DAG.A través del simulador se realizó un estudio exhaustivode cuales son los casos que proporcionanun mejor makespan tras finalizar un DAG y cualesno. Este estudio se ha integrado en el algoritmo quedecide si se debe o no realizar auto-adaptación.Finalmente se han ejecutado DAGS reales con lapolítica SAHEFT en escenarios heterogéneos mediantela herramienta SchedFlow.De la experimentación real se concluye que, en casoque los escenarios sufran variaciones importantes yque el error que se cometa en la estimación seasuperior a un 5%, la política SAHEFT mejora elmakespan mas de un 15% respecto a HEFT. La reducciónde sobrecarga que proporciona SAHEFT esimportante, del orden de 40% o superior respectoal hecho de reaccionar siempre ante la detección decualquier cambio.AgradecimientosEste artículo ha sido financiado por el MEC-MICINN Spain mediante el proyecto TIN2007-64974tional Journal of Computer Science and Network Security,VOL.9 No.4, April 2009[14] D.A. Brown, P.R. Brady, A. Dietz, J. Cao, B. Johnsonand J. McNabb, A case study on the use of workflow technologiesfor scientific analysis: gravitational wave dataanalysis, Workflows for e-Science, Springer (2006).[15] Douglas Thain, Todd Tannenbaum, and Miron Livny,Distributed Computing in Practice: The Condor Experience,Concurrency and Computation: Practice and ExperienceVolume 17. Issue 2-4. pp: 323-356, 2005[16] Rich Wolski, Forecasting Network Performance to SupportDynamic Scheduling Using the Network Weather Service,High Performance Distributed Computing, 1997.Proceedings. The Sixth IEEE International Symposiumon, pp 316-325, 1997[17] Gustavo Martinez, Elisa Heymann, Miguel Angel Senar,Emilio Luque, Barton P. Miller Using SchedFlow for PerformanceEvaluation of Workflow Applications Workflowsin Support of Large-Scale Science (WORKS), 5th Workshopon, pp. 1-8, 2010[18] Maria M. Lopez, Elisa Heymann, Miguel Angel Senar,Analysis of Dynamic Heuristics for Workflow Schedulingon Grid Systems, ispdc, pp.199-207, Proceedings ofThe Fifth International Symposium on Parallel and DistributedComputing (ISPDC’06), 2006[19] W.M.P. van der Aalst, K.M. van Hee and G.J. Houben,Modelling and Analysing Workflow using a Petri-netBased Approach, 2nd Workshop on Computer-supportedCooperative Work, Petri Nets Related Formalisms, pp. 31-50,1994.[20] Jia Yu and Rajkumar Buyya A Taxonomy of WorkflowManagement Systems for Grid Computing, Journal ofGrid Computing Volume 3, Numbers 3-4, 171-200, 2005Referencias[1] Min-You Wu, Wei Shu, Hong Zhang. Segmented Min-Min:A Static Mapping Algorithm for Meta-Tasks on HeterogeneousComputing Systems., 9th Heterogeneous ComputingWorkshop, 2000, pp. 375-385, 2000.[2] Y.-K. Kwok, I. Ahmad, Static scheduling algorithms forallocating directed task graphs to multiprocessors., ACMComputing Surveys, 31(4), pp. 406-471, 1999.[3] H. Topcuoglu, S. Hariri, and M.Y. Wu. Performance-Effective and Low-Complexity Task Scheduling for HeterogeneousComputing., IEEE Trans. Parallel and DistributedSystems, Vol. 13, no. 3, pp. 260-274, 2002.[4] A. Mandal et al. Scheduling Strategies for MappingApplication Workflows onto the Grid. In 14th IEEESymposium on High Performance Distributed Computing(HPDC 2005). IEEE Computer Society Press, pp. 4, 2005.[5] R Sakellariou, H Zhao. A Hybrid Heuristic for DAG Schedulingon Heterogeneous Systems. Proc. Of the 13th HeterogeneousComputing Workshop (HCW 04), pp. 111,2004.[6] H Topcuoglu, S Hariri, Min-You Wu. Task SchedulingAlgorithms for Heterogeneous Processors. Eighth HeterogeneousComputing Workshop, hcw,pp.3-14, 1999.[7] Louis-Claude Canon and Emmanuel Jeannot A Comparisonof Robustness Metrics for Scheduling DAGs on HeterogeneousSystems, IEEE International Conference onCluster Computing, pp.558-567, 2007[8] Louis-Claude Canon and Emmanuel Jeannot, Rizos Sakellariouand Wei Zheng, Comparative evaluation ofthe robustness of DAG scheduling heuristics, GridComputing,pp.73-84, 2008,[9] R Sakellariou, H Zhao, A low-cost rescheduling policy forefficient mapping of workflows on grid systems. ScientificProgramming, 12 (4), pp. 253-262, Dec. 2004.[10] Zhifeng Yu and Weisong Shi, An Adaptive ReschedulingStrategy for Grid Workflow Applications, Proceedings ofthe 21st IPDPS 2007.[11] Sung Ho Chin,Taeweon Suh,Heon Chang Yu, Adaptiveservice scheduling for workflow applications in Service-Oriented Grid, The Journal of Supercomputing Volume52, Number 3, 253-283, 2009[12] Berriman, G. Laity, A. and et al, Montage: The Architectureand Scientific Applications of a National VirtualObservatory Service for Computing Astronomical ImageMosaics, In. Proc. of Earth Sciences Technology Conference,Maryland USA, 2006[13] Enis Afgant and Purushotham Bangaloret, DynamicBLAST a Grid Enabled BLAST, IJCSNS Interna-JP2011-481