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Efficient Scheduling of Scientific Workflows Using Hot Metadata in a Multisite Cloud.

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Title: Efficient Scheduling of Scientific Workflows Using Hot Metadata in a Multisite Cloud.
Authors: Liu, Ji1 (AUTHOR) ji.liu@inria.fr, Pineda, Luis2 (AUTHOR) luis.pineda-morales@inria.fr, Pacitti, Esther1 (AUTHOR) esther.pacitti@lirmm.fr, Costan, Alexandru2 (AUTHOR) alexandru.costan@irisa.fr, Valduriez, Patrick1 (AUTHOR) patrick.valduriez@inria.fr, Antoniu, Gabriel2 (AUTHOR) gabriel.antoniu@inria.fr, Mattoso, Marta3 (AUTHOR) marta@cos.ufrj.br
Source: IEEE Transactions on Knowledge & Data Engineering. Oct2019, Vol. 31 Issue 10, p1940-1953. 14p.
Subjects: Adobe Flash (Computer software), Metadata, Workflow management, Workflow management systems, Scheduling, Server farms (Computer network management), Sovereign wealth funds
Abstract: Large-scale, data-intensive scientific applications are often expressed as scientific workflows (SWfs). In this paper, we consider the problem of efficient scheduling of a large SWf in a multisite cloud, i.e., a cloud with geo-distributed cloud data centers (sites). The reasons for using multiple cloud sites to run a SWf are that data is already distributed, the necessary resources exceed the limits at a single site, or the monetary cost is lower. In a multisite cloud, metadata management has a critical impact on the efficiency of SWf scheduling as it provides a global view of data location and enables task tracking during execution. Thus, it should be readily available to the system at any given time. While it has been shown that efficient metadata handling plays a key role in performance, little research has targeted this issue in multisite cloud. In this paper, we propose to identify and exploit hot metadata (frequently accessed metadata) for efficient SWf scheduling in a multisite cloud, using a distributed approach. We implemented our approach within a scientific workflow management system, which shows that our approach reduces the execution time of highly parallel jobs up to 64 percent and that of the whole SWfs up to 55 percent. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Efficient Scheduling of Scientific Workflows Using Hot Metadata in a Multisite Cloud.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Ji%22">Liu, Ji</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ji.liu@inria.fr</i><br /><searchLink fieldCode="AR" term="%22Pineda%2C+Luis%22">Pineda, Luis</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> luis.pineda-morales@inria.fr</i><br /><searchLink fieldCode="AR" term="%22Pacitti%2C+Esther%22">Pacitti, Esther</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> esther.pacitti@lirmm.fr</i><br /><searchLink fieldCode="AR" term="%22Costan%2C+Alexandru%22">Costan, Alexandru</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> alexandru.costan@irisa.fr</i><br /><searchLink fieldCode="AR" term="%22Valduriez%2C+Patrick%22">Valduriez, Patrick</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> patrick.valduriez@inria.fr</i><br /><searchLink fieldCode="AR" term="%22Antoniu%2C+Gabriel%22">Antoniu, Gabriel</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> gabriel.antoniu@inria.fr</i><br /><searchLink fieldCode="AR" term="%22Mattoso%2C+Marta%22">Mattoso, Marta</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> marta@cos.ufrj.br</i>
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  Data: Large-scale, data-intensive scientific applications are often expressed as scientific workflows (SWfs). In this paper, we consider the problem of efficient scheduling of a large SWf in a multisite cloud, i.e., a cloud with geo-distributed cloud data centers (sites). The reasons for using multiple cloud sites to run a SWf are that data is already distributed, the necessary resources exceed the limits at a single site, or the monetary cost is lower. In a multisite cloud, metadata management has a critical impact on the efficiency of SWf scheduling as it provides a global view of data location and enables task tracking during execution. Thus, it should be readily available to the system at any given time. While it has been shown that efficient metadata handling plays a key role in performance, little research has targeted this issue in multisite cloud. In this paper, we propose to identify and exploit hot metadata (frequently accessed metadata) for efficient SWf scheduling in a multisite cloud, using a distributed approach. We implemented our approach within a scientific workflow management system, which shows that our approach reduces the execution time of highly parallel jobs up to 64 percent and that of the whole SWfs up to 55 percent. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Knowledge & Data Engineering is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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        Value: 10.1109/TKDE.2018.2867857
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        Text: English
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        PageCount: 14
        StartPage: 1940
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        Type: general
      – SubjectFull: Metadata
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      – SubjectFull: Workflow management
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      – SubjectFull: Scheduling
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      – SubjectFull: Server farms (Computer network management)
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      – SubjectFull: Sovereign wealth funds
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      – TitleFull: Efficient Scheduling of Scientific Workflows Using Hot Metadata in a Multisite Cloud.
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              M: 10
              Text: Oct2019
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              Y: 2019
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