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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Vydáno v:	Future generation computer systems Ročník 63; s. 76 - 95
Hlavní autoři:	Liu, Ji, Pacitti, Esther, Valduriez, Patrick, de Oliveira, Daniel, Mattoso, Marta
Médium:	Journal Article Publikace
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.10.2016 Elsevier
Témata:	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
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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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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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