Dynamic energy-aware scheduling for parallel task-based application in cloud computing.
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| Title: | Dynamic energy-aware scheduling for parallel task-based application in cloud computing. |
|---|---|
| Authors: | Juarez, Fredy1,2, Ejarque, Jorge1 jorge.ejarque@bsc.es, Badia, Rosa M.1,3 |
| Source: | Future Generation Computer Systems. Jan2018 Part 1, Vol. 78, p257-271. 15p. |
| Subjects: | Computer scheduling, Distributed computing software, Parallel computers, Cloud computing, Energy consumption |
| Abstract: | Green Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently task-based applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling. [ABSTRACT FROM AUTHOR] |
| Copyright of Future Generation Computer Systems is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 125232951 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Juarez%2C+Fredy%22">Juarez, Fredy</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ejarque%2C+Jorge%22">Ejarque, Jorge</searchLink><relatesTo>1</relatesTo><i> jorge.ejarque@bsc.es</i><br /><searchLink fieldCode="AR" term="%22Badia%2C+Rosa+M%2E%22">Badia, Rosa M.</searchLink><relatesTo>1,3</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Future+Generation+Computer+Systems%22">Future Generation Computer Systems</searchLink>. Jan2018 Part 1, Vol. 78, p257-271. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Computer+scheduling%22">Computer scheduling</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing+software%22">Distributed computing software</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+computers%22">Parallel computers</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Energy+consumption%22">Energy consumption</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Green Computing is a recent trend in computer science, which tries to reduce the energy consumption and carbon footprint produced by computers on distributed platforms such as clusters, grids, and clouds. Traditional scheduling solutions attempt to minimize processing times without taking into account the energetic cost. One of the methods for reducing energy consumption is providing scheduling policies in order to allocate tasks on specific resources that impact over the processing times and energy consumption. In this paper, we propose a real-time dynamic scheduling system to execute efficiently task-based applications on distributed computing platforms in order to minimize the energy consumption. Scheduling tasks on multiprocessors is a well known NP-hard problem and optimal solution of these problems is not feasible, we present a polynomial-time algorithm that combines a set of heuristic rules and a resource allocation technique in order to get good solutions on an affordable time scale. The proposed algorithm minimizes a multi-objective function which combines the energy-consumption and execution time according to the energy-performance importance factor provided by the resource provider or user, also taking into account sequence-dependent setup times between tasks, setup times and down times for virtual machines (VM) and energy profiles for different architectures. A prototype implementation of the scheduler has been tested with different kinds of DAG generated at random as well as on real task-based COMPSs applications. We have tested the system with different size instances and importance factors, and we have evaluated which combination provides a better solution and energy savings. Moreover, we have also evaluated the introduced overhead by measuring the time for getting the scheduling solutions for a different number of tasks, kinds of DAG, and resources, concluding that our method is suitable for run-time scheduling. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Future Generation Computer Systems is the property of Elsevier B.V. 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: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.future.2016.06.029 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 257 Subjects: – SubjectFull: Computer scheduling Type: general – SubjectFull: Distributed computing software Type: general – SubjectFull: Parallel computers Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Energy consumption Type: general Titles: – TitleFull: Dynamic energy-aware scheduling for parallel task-based application in cloud computing. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Juarez, Fredy – PersonEntity: Name: NameFull: Ejarque, Jorge – PersonEntity: Name: NameFull: Badia, Rosa M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2018 Part 1 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 0167739X Numbering: – Type: volume Value: 78 Titles: – TitleFull: Future Generation Computer Systems Type: main |
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