Multi-objective scheduling of Scientific Workflows in multisite clouds

Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographic...

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Veröffentlicht in:	Future generation computer systems Jg. 63; S. 76 - 95
Hauptverfasser:	Liu, Ji, Pacitti, Esther, Valduriez, Patrick, de Oliveira, Daniel, Mattoso, Marta
Format:	Journal Article Verlag
Sprache:	Englisch
Veröffentlicht:	Elsevier B.V 01.10.2016 Elsevier
Schlagworte:	Algorismes computacionals Algorithms and architectures for advanced scientific computing Cicle de treball Computer Science Enginyeria electrònica Multi-objective scheduling Multisite cloud Parallel execution Parallel processing (Electronic computers) Processament en paral·lel (Ordinadors) Scientific workflow Scientific workflow management system Workflow computing systems Àrees temàtiques de la UPC Scientific workflow management system Multisite cloud Parallel execution Multi-objective scheduling Scientific workflow multi-objective scheduling parallel execution multisite cloud scientific workflow management system
ISSN:	0167-739X, 1872-7115
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Abstract	Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among different cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution consists of a multi-objective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach. •A multi-objective cost model that includes execution time and monetary costs.•A Single Site VM Provisioning (SSVP) approach, to generate VM provisioning plans.•ActGreedy, an efficient scheduling algorithm for SWf execution in multisite cloud.•An extensive experimental evaluation in Microsoft Azure using the SciEvol SWf.
AbstractList	Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among different cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution consists of a multi-objective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach. Work partially funded by EU H2020 Programme and MCTI/RNP-Brazil (HPC4E grant agreement number 689772), CNPq, FAPERJ, and INRIA (MUSIC project), Microsoft (ZcloudFlow project) and performed in the context of the Computational Biology Institute (www.ibc-montpellier.fr). We would like to thank Kary Ocaña for her help in modeling and executing the SciEvol SWf. Peer Reviewed Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites(or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among dierent cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution includes a multiobjective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach. Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among different cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution consists of a multi-objective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach. •A multi-objective cost model that includes execution time and monetary costs.•A Single Site VM Provisioning (SSVP) approach, to generate VM provisioning plans.•ActGreedy, an efficient scheduling algorithm for SWf execution in multisite cloud.•An extensive experimental evaluation in Microsoft Azure using the SciEvol SWf.
Author	Liu, Ji de Oliveira, Daniel Mattoso, Marta Pacitti, Esther Valduriez, Patrick
Author_xml	– sequence: 1 givenname: Ji orcidid: 0000-0003-4710-5697 surname: Liu fullname: Liu, Ji email: ji.liu@inria.fr organization: Inria, Microsoft-Inria Joint Centre, LIRMM and University of Montpellier, France – sequence: 2 givenname: Esther surname: Pacitti fullname: Pacitti, Esther organization: Inria, Microsoft-Inria Joint Centre, LIRMM and University of Montpellier, France – sequence: 3 givenname: Patrick surname: Valduriez fullname: Valduriez, Patrick organization: Inria, Microsoft-Inria Joint Centre, LIRMM and University of Montpellier, France – sequence: 4 givenname: Daniel surname: de Oliveira fullname: de Oliveira, Daniel organization: Institute of Computing, Fluminense Federal University, Niteroi, Brazil – sequence: 5 givenname: Marta surname: Mattoso fullname: Mattoso, Marta organization: COPPE, Federal University of Rio de Janeiro, Brazil
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Cites_doi	10.1145/2807591.2807636 10.1109/TAC.1963.1105511 10.1109/CANET.2007.4401694 10.1016/j.future.2008.06.012 10.1002/cpe.3003 10.1109/CLUSTER.2014.6968789 10.1109/eScience.2013.40 10.1016/j.jpdc.2009.05.002 10.1109/ICPP.2015.93 10.1145/1084805.1084816 10.1007/s00158-003-0368-6 10.1016/S0950-5849(02)00025-3 10.1109/IPDPS.2008.4536445 10.1109/71.993206 10.1016/j.future.2012.12.019 10.2218/ijdc.v7i2.232 10.1109/IPDPS.2007.370305 10.1109/e-Science.2009.49 10.1002/cpe.3032 10.1007/s10723-012-9227-2 10.1109/TCC.2014.2303077 10.1109/HCW.1999.765094 10.1016/j.jpdc.2013.12.004
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Keywords	Scientific workflow management system Multisite cloud Parallel execution Multi-objective scheduling Scientific workflow multi-objective scheduling parallel execution multisite cloud scientific workflow management system
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References	Amazon ec2, Amazon elastic compute cloud (Amazon ec2). W. Chen, R.F. Da Silva, E. Deelman, R. Sakellariou, Balanced task clustering in scientific workflows, in: IEEE 9th Int. Conf. on e-Science, 2013, pp. 188–195. Ogasawara, Dias, Silva, Chirigati, de~Oliveira, Porto, Valduriez, Mattoso (br000140) 2013; 25 Liu, Pacitti, Valduriez, Mattoso (br000010) 2015 de~Oliveira, Ocaña, Baião, Mattoso (br000025) 2012; 10 J. Liu, E. Pacitti, P. Valduriez, M. Mattoso, Parallelization of Scientific Workflows in the Cloud, Research Report RR-8565, 2014. Montage. Topcuouglu, Hariri, Wu (br000065) 2002; 13 C. Anglano, M. Canonico, Scheduling algorithms for multiple bag-of-task applications on desktop grids: A knowledge-free approach, in: 22nd IEEE Int. Symposium on Parallel and Distributed Processing, IPDPS, 2008, pp. 1–8. Littauer, Ram, Ludäscher, Michener, Koskela (br000180) 2012; 7 Marler, Arora (br000110) 2004; 26 Chang, Son, Kim (br000145) 2002; 44 K. Etminani, M. Naghibzadeh, A min-min max–min selective algorihtm for grid task scheduling, in: The Third IEEE/IFIP Int. Conf. in Central Asia on Internet, ICI 2007, 2007, pp. 1–7. Mohammadi, Prodan, Fahringer (br000085) 2014; 74 I. Sardiña, C. Boeres, L. de A. Drummond, An efficient weighted bi-objective scheduling algorithm for heterogeneous systems, in: Euro-Par 2009—Parallel Processing Workshops, vol. 6043, 2010, pp. 102–111. Deelman, Gannon, Shields, Taylor (br000005) 2009; 25 De Oliveira, Ocaña, Ogasawara, Dias, GonçAlves, Baião, Mattoso (br000115) 2013; 29 Özsu, Valduriez (br000135) 2011 M. Maheswaran, S. Ali, H.J. Siegel, D. Hensgen, R.F. Freund, Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems, in: 8th Heterogeneous Computing Workshop, 1999, p. 30. Rahman, Hassan, Ranjan, Buyya (br000055) 2013; 25 Sequence identifier. Oma genome database. Z. Yu, W. Shi, An adaptive rescheduling strategy for grid workflow applications, in: IEEE Int. Parallel and Distributed Processing Symposium, IPDPS, 2007, pp. 1–8. R. Coutinho, L. Drummond, Y. Frota, D. de Oliveira, K. Ocana, Evaluating grasp-based cloud dimensioning for comparative genomics: A practical approach, in: 2014 IEEE Int. Conf. on Cluster Computing, CLUSTER, 2014, pp. 371–379. . Microsoft Azure. Computing capacity for a CPU. Duan, Prodan, Li (br000040) 2014; 2 S. Blagodurov, A. Fedorova, E. Vinnik, T. Dwyer, F. Hermenier, Multi-objective job placement in clusters, in: Proceedings of the Int. Conf. for High Performance Computing, Networking, Storage and Analysis, SC, 2015, pp. 66:1–66:12. Zadeh (br000105) 1963; 8 Sun, Chen (br000150) 2010; 70 Ocan¯a, de~Oliveira, Horta, Dias, Ogasawara, Mattoso (br000030) 2012; vol. 7409 M.A. Rodriguez, R. Buyya, A responsive Knapsack-based algorithm for resource provisioning and scheduling of scientific workflows in clouds, in: 44th Int. Conf. on Parallel Processing, ICPP, 2015. S. Smanchat, M. Indrawan, S. Ling, C. Enticott, D. Abramson, Scheduling multiple parameter sweep workflow instances on the grid, in: 5th IEEE Int. Conf. on e-Science, 2009, pp. 300–306. Wieczorek, Prodan, Fahringer (br000075) 2005; 34 J. Liu, V. Silva, E. Pacitti, P. Valduriez, M. Mattoso, Scientific workflow partitioning in multi-site clouds, in: BigDataCloud’2014: 3rd Workshop on Big Data Management in Clouds in conjunction with Euro-Par 2014, 2014, p. 12. J. Yu, R. Buyya, C.K. Tham, Cost-based scheduling of scientific workflow applications on utility grids, in: First Int. Conf. on e-Science and Grid Computing, 2005, pp. 140–147. Ogasawara (10.1016/j.future.2016.04.014_br000140) 2013; 25 Topcuouglu (10.1016/j.future.2016.04.014_br000065) 2002; 13 10.1016/j.future.2016.04.014_br000080 10.1016/j.future.2016.04.014_br000095 10.1016/j.future.2016.04.014_br000050 Mohammadi (10.1016/j.future.2016.04.014_br000085) 2014; 74 Sun (10.1016/j.future.2016.04.014_br000150) 2010; 70 10.1016/j.future.2016.04.014_br000170 10.1016/j.future.2016.04.014_br000155 10.1016/j.future.2016.04.014_br000175 10.1016/j.future.2016.04.014_br000130 de~Oliveira (10.1016/j.future.2016.04.014_br000025) 2012; 10 Ocan¯a (10.1016/j.future.2016.04.014_br000030) 2012; vol. 7409 10.1016/j.future.2016.04.014_br000015 Zadeh (10.1016/j.future.2016.04.014_br000105) 1963; 8 10.1016/j.future.2016.04.014_br000035 Deelman (10.1016/j.future.2016.04.014_br000005) 2009; 25 Marler (10.1016/j.future.2016.04.014_br000110) 2004; 26 Wieczorek (10.1016/j.future.2016.04.014_br000075) 2005; 34 Rahman (10.1016/j.future.2016.04.014_br000055) 2013; 25 Littauer (10.1016/j.future.2016.04.014_br000180) 2012; 7 10.1016/j.future.2016.04.014_br000070 10.1016/j.future.2016.04.014_br000090 10.1016/j.future.2016.04.014_br000160 10.1016/j.future.2016.04.014_br000060 10.1016/j.future.2016.04.014_br000045 10.1016/j.future.2016.04.014_br000100 Liu (10.1016/j.future.2016.04.014_br000010) 2015 Chang (10.1016/j.future.2016.04.014_br000145) 2002; 44 10.1016/j.future.2016.04.014_br000165 10.1016/j.future.2016.04.014_br000120 10.1016/j.future.2016.04.014_br000020 Özsu (10.1016/j.future.2016.04.014_br000135) 2011 10.1016/j.future.2016.04.014_br000125 De Oliveira (10.1016/j.future.2016.04.014_br000115) 2013; 29 Duan (10.1016/j.future.2016.04.014_br000040) 2014; 2
References_xml	– volume: 8 start-page: 59 year: 1963 end-page: 60 ident: br000105 article-title: Optimality and non-scalar-valued performance criteria publication-title: IEEE Trans. Automat. Control – volume: 26 start-page: 369 year: 2004 end-page: 395 ident: br000110 article-title: Survey of multi-objective optimization methods for engineering publication-title: Struct. Multidiscip. Optim. – reference: Oma genome database. – reference: W. Chen, R.F. Da Silva, E. Deelman, R. Sakellariou, Balanced task clustering in scientific workflows, in: IEEE 9th Int. Conf. on e-Science, 2013, pp. 188–195. – volume: 29 start-page: 1816 year: 2013 end-page: 1825 ident: br000115 article-title: Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows publication-title: Future Gener. Comput. Syst. – volume: 13 start-page: 260 year: 2002 end-page: 274 ident: br000065 article-title: Performance-effective and low-complexity task scheduling for heterogeneous computing publication-title: IEEE Trans. Parallel Distrib. Syst. – reference: Z. Yu, W. Shi, An adaptive rescheduling strategy for grid workflow applications, in: IEEE Int. Parallel and Distributed Processing Symposium, IPDPS, 2007, pp. 1–8. – volume: 34 start-page: 56 year: 2005 end-page: 62 ident: br000075 article-title: Scheduling of scientific workflows in the ASKALON grid environment publication-title: SIGMOD Rec. – volume: 10 start-page: 521 year: 2012 end-page: 552 ident: br000025 article-title: A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds publication-title: J. Grid Comput. – year: 2011 ident: br000135 article-title: Principles of Distributed Database Systems – reference: S. Smanchat, M. Indrawan, S. Ling, C. Enticott, D. Abramson, Scheduling multiple parameter sweep workflow instances on the grid, in: 5th IEEE Int. Conf. on e-Science, 2009, pp. 300–306. – reference: Amazon ec2, Amazon elastic compute cloud (Amazon ec2). – reference: R. Coutinho, L. Drummond, Y. Frota, D. de Oliveira, K. Ocana, Evaluating grasp-based cloud dimensioning for comparative genomics: A practical approach, in: 2014 IEEE Int. Conf. on Cluster Computing, CLUSTER, 2014, pp. 371–379. – start-page: 1 year: 2015 end-page: 37 ident: br000010 article-title: A survey of data-intensive scientific workflow management publication-title: J. Grid Comput. – reference: J. Liu, V. Silva, E. Pacitti, P. Valduriez, M. Mattoso, Scientific workflow partitioning in multi-site clouds, in: BigDataCloud’2014: 3rd Workshop on Big Data Management in Clouds in conjunction with Euro-Par 2014, 2014, p. 12. – volume: 74 start-page: 2152 year: 2014 end-page: 2165 ident: br000085 article-title: Multi-objective list scheduling of workflow applications in distributed computing infrastructures publication-title: J. Parallel Distrib. Comput. – reference: J. Yu, R. Buyya, C.K. Tham, Cost-based scheduling of scientific workflow applications on utility grids, in: First Int. Conf. on e-Science and Grid Computing, 2005, pp. 140–147. – reference: J. Liu, E. Pacitti, P. Valduriez, M. Mattoso, Parallelization of Scientific Workflows in the Cloud, Research Report RR-8565, 2014. – volume: vol. 7409 start-page: 179 year: 2012 end-page: 191 ident: br000030 article-title: Exploring molecular evolution reconstruction using a parallel cloud based scientific workflow publication-title: Advances in Bioinformatics and Computational Biology – reference: Sequence identifier. – reference: Microsoft Azure. – reference: K. Etminani, M. Naghibzadeh, A min-min max–min selective algorihtm for grid task scheduling, in: The Third IEEE/IFIP Int. Conf. in Central Asia on Internet, ICI 2007, 2007, pp. 1–7. – volume: 25 start-page: 2327 year: 2013 end-page: 2341 ident: br000140 article-title: Chiron: a parallel engine for algebraic scientific workflows publication-title: Concurr. Comput.:Pract. Exp. – reference: . – reference: S. Blagodurov, A. Fedorova, E. Vinnik, T. Dwyer, F. Hermenier, Multi-objective job placement in clusters, in: Proceedings of the Int. Conf. for High Performance Computing, Networking, Storage and Analysis, SC, 2015, pp. 66:1–66:12. – reference: M. Maheswaran, S. Ali, H.J. Siegel, D. Hensgen, R.F. Freund, Dynamic matching and scheduling of a class of independent tasks onto heterogeneous computing systems, in: 8th Heterogeneous Computing Workshop, 1999, p. 30. – volume: 44 start-page: 405 year: 2002 end-page: 417 ident: br000145 article-title: Critical path identification in the context of a workflow publication-title: Inf. Softw. Technol. – volume: 25 start-page: 528 year: 2009 end-page: 540 ident: br000005 article-title: Workflows and e-science: An overview of workflow system features and capabilities publication-title: Future Gener. Comput. Syst. – volume: 25 start-page: 1816 year: 2013 end-page: 1842 ident: br000055 article-title: Adaptive workflow scheduling for dynamic grid and cloud computing environment publication-title: Concurr. Comput.:Pract. Exp. – reference: I. Sardiña, C. Boeres, L. de A. Drummond, An efficient weighted bi-objective scheduling algorithm for heterogeneous systems, in: Euro-Par 2009—Parallel Processing Workshops, vol. 6043, 2010, pp. 102–111. – reference: C. Anglano, M. Canonico, Scheduling algorithms for multiple bag-of-task applications on desktop grids: A knowledge-free approach, in: 22nd IEEE Int. Symposium on Parallel and Distributed Processing, IPDPS, 2008, pp. 1–8. – reference: M.A. Rodriguez, R. Buyya, A responsive Knapsack-based algorithm for resource provisioning and scheduling of scientific workflows in clouds, in: 44th Int. Conf. on Parallel Processing, ICPP, 2015. – volume: 2 start-page: 29 year: 2014 end-page: 42 ident: br000040 article-title: Multi-objective game theoretic schedulingof bag-of-tasks workflows on hybrid clouds publication-title: IEEE Trans. Cloud Comput. – reference: Computing capacity for a CPU. – reference: Montage. – volume: 7 start-page: 92 year: 2012 end-page: 100 ident: br000180 article-title: Trends in use of scientific workflows: Insights from a public repository and recommendations for best practice publication-title: Int. J. Digit. Curat. – volume: 70 start-page: 183 year: 2010 end-page: 188 ident: br000150 article-title: Reevaluating Amdahl’s law in the multicore era publication-title: J. Parallel Distrib. Comput. – ident: 10.1016/j.future.2016.04.014_br000100 doi: 10.1145/2807591.2807636 – volume: 8 start-page: 59 issue: 1 year: 1963 ident: 10.1016/j.future.2016.04.014_br000105 article-title: Optimality and non-scalar-valued performance criteria publication-title: IEEE Trans. Automat. Control doi: 10.1109/TAC.1963.1105511 – ident: 10.1016/j.future.2016.04.014_br000080 doi: 10.1109/CANET.2007.4401694 – ident: 10.1016/j.future.2016.04.014_br000155 – volume: 25 start-page: 528 issue: 5 year: 2009 ident: 10.1016/j.future.2016.04.014_br000005 article-title: Workflows and e-science: An overview of workflow system features and capabilities publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2008.06.012 – volume: 25 start-page: 1816 issue: 13 year: 2013 ident: 10.1016/j.future.2016.04.014_br000055 article-title: Adaptive workflow scheduling for dynamic grid and cloud computing environment publication-title: Concurr. Comput.:Pract. Exp. doi: 10.1002/cpe.3003 – ident: 10.1016/j.future.2016.04.014_br000095 doi: 10.1109/CLUSTER.2014.6968789 – ident: 10.1016/j.future.2016.04.014_br000130 doi: 10.1109/eScience.2013.40 – volume: 70 start-page: 183 issue: 2 year: 2010 ident: 10.1016/j.future.2016.04.014_br000150 article-title: Reevaluating Amdahl’s law in the multicore era publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2009.05.002 – ident: 10.1016/j.future.2016.04.014_br000015 – ident: 10.1016/j.future.2016.04.014_br000090 doi: 10.1109/ICPP.2015.93 – ident: 10.1016/j.future.2016.04.014_br000120 – ident: 10.1016/j.future.2016.04.014_br000035 – ident: 10.1016/j.future.2016.04.014_br000170 – ident: 10.1016/j.future.2016.04.014_br000165 – volume: vol. 7409 start-page: 179 year: 2012 ident: 10.1016/j.future.2016.04.014_br000030 article-title: Exploring molecular evolution reconstruction using a parallel cloud based scientific workflow – volume: 34 start-page: 56 issue: 3 year: 2005 ident: 10.1016/j.future.2016.04.014_br000075 article-title: Scheduling of scientific workflows in the ASKALON grid environment publication-title: SIGMOD Rec. doi: 10.1145/1084805.1084816 – volume: 26 start-page: 369 issue: 6 year: 2004 ident: 10.1016/j.future.2016.04.014_br000110 article-title: Survey of multi-objective optimization methods for engineering publication-title: Struct. Multidiscip. Optim. doi: 10.1007/s00158-003-0368-6 – year: 2011 ident: 10.1016/j.future.2016.04.014_br000135 – volume: 44 start-page: 405 issue: 7 year: 2002 ident: 10.1016/j.future.2016.04.014_br000145 article-title: Critical path identification in the context of a workflow publication-title: Inf. Softw. Technol. doi: 10.1016/S0950-5849(02)00025-3 – ident: 10.1016/j.future.2016.04.014_br000045 doi: 10.1109/IPDPS.2008.4536445 – volume: 13 start-page: 260 issue: 3 year: 2002 ident: 10.1016/j.future.2016.04.014_br000065 article-title: Performance-effective and low-complexity task scheduling for heterogeneous computing publication-title: IEEE Trans. Parallel Distrib. Syst. doi: 10.1109/71.993206 – volume: 29 start-page: 1816 issue: 7 year: 2013 ident: 10.1016/j.future.2016.04.014_br000115 article-title: Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows publication-title: Future Gener. Comput. Syst. doi: 10.1016/j.future.2012.12.019 – ident: 10.1016/j.future.2016.04.014_br000125 – start-page: 1 year: 2015 ident: 10.1016/j.future.2016.04.014_br000010 article-title: A survey of data-intensive scientific workflow management publication-title: J. Grid Comput. – ident: 10.1016/j.future.2016.04.014_br000160 – volume: 7 start-page: 92 issue: 2 year: 2012 ident: 10.1016/j.future.2016.04.014_br000180 article-title: Trends in use of scientific workflows: Insights from a public repository and recommendations for best practice publication-title: Int. J. Digit. Curat. doi: 10.2218/ijdc.v7i2.232 – ident: 10.1016/j.future.2016.04.014_br000070 doi: 10.1109/IPDPS.2007.370305 – ident: 10.1016/j.future.2016.04.014_br000060 doi: 10.1109/e-Science.2009.49 – volume: 25 start-page: 2327 issue: 16 year: 2013 ident: 10.1016/j.future.2016.04.014_br000140 article-title: Chiron: a parallel engine for algebraic scientific workflows publication-title: Concurr. Comput.:Pract. Exp. doi: 10.1002/cpe.3032 – volume: 10 start-page: 521 issue: 3 year: 2012 ident: 10.1016/j.future.2016.04.014_br000025 article-title: A provenance-based adaptive scheduling heuristic for parallel scientific workflows in clouds publication-title: J. Grid Comput. doi: 10.1007/s10723-012-9227-2 – volume: 2 start-page: 29 issue: 1 year: 2014 ident: 10.1016/j.future.2016.04.014_br000040 article-title: Multi-objective game theoretic schedulingof bag-of-tasks workflows on hybrid clouds publication-title: IEEE Trans. Cloud Comput. doi: 10.1109/TCC.2014.2303077 – ident: 10.1016/j.future.2016.04.014_br000175 – ident: 10.1016/j.future.2016.04.014_br000050 doi: 10.1109/HCW.1999.765094 – ident: 10.1016/j.future.2016.04.014_br000020 – volume: 74 start-page: 2152 issue: 3 year: 2014 ident: 10.1016/j.future.2016.04.014_br000085 article-title: Multi-objective list scheduling of workflow applications in distributed computing infrastructures publication-title: J. Parallel Distrib. Comput. doi: 10.1016/j.jpdc.2013.12.004
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Snippet	Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with... Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites(or data centers), each with...
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SubjectTerms	Algorismes computacionals Algorithms and architectures for advanced scientific computing Cicle de treball Computer Science Enginyeria electrònica Multi-objective scheduling Multisite cloud Parallel execution Parallel processing (Electronic computers) Processament en paral·lel (Ordinadors) Scientific workflow Scientific workflow management system Workflow computing systems Àrees temàtiques de la UPC
Title	Multi-objective scheduling of Scientific Workflows in multisite clouds
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