An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers.
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| Title: | An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers. |
|---|---|
| Authors: | Khamse-Ashari, Jalal, Lambadaris, Ioannis, Kesidis, George, Urgaonkar, Bhuvan, Zhao, Yiqiang |
| Source: | IEEE Transactions on Parallel & Distributed Systems. 12/1/2018, Vol. 29 Issue 12, p2686-2699. 14p. |
| Subjects: | Client/server computing management, Resource allocation, Cloud computing, Distributed algorithms, Pareto analysis |
| Abstract: | Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a newserver-based approach; each server allocates resources by maximizing a per-serverutility function. We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposedPer-Server Dominant Share Fairness). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Subsequently, we show how the proposed mechanism could be implemented in a distributed fashion. Finally, we carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Transactions on Parallel & Distributed Systems 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 132967326 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Khamse-Ashari%2C+Jalal%22">Khamse-Ashari, Jalal</searchLink><br /><searchLink fieldCode="AR" term="%22Lambadaris%2C+Ioannis%22">Lambadaris, Ioannis</searchLink><br /><searchLink fieldCode="AR" term="%22Kesidis%2C+George%22">Kesidis, George</searchLink><br /><searchLink fieldCode="AR" term="%22Urgaonkar%2C+Bhuvan%22">Urgaonkar, Bhuvan</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yiqiang%22">Zhao, Yiqiang</searchLink> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Parallel+%26+Distributed+Systems%22">IEEE Transactions on Parallel & Distributed Systems</searchLink>. 12/1/2018, Vol. 29 Issue 12, p2686-2699. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Client%2Fserver+computing+management%22">Client/server computing management</searchLink><br /><searchLink fieldCode="DE" term="%22Resource+allocation%22">Resource allocation</searchLink><br /><searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+algorithms%22">Distributed algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Pareto+analysis%22">Pareto analysis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness and its follow-up work. To overcome such limitations, we propose a newserver-based approach; each server allocates resources by maximizing a per-serverutility function. We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposedPer-Server Dominant Share Fairness). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Subsequently, we show how the proposed mechanism could be implemented in a distributed fashion. Finally, we carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Transactions on Parallel & Distributed Systems 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: BibEntity: Identifiers: – Type: doi Value: 10.1109/TPDS.2018.2841915 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 2686 Subjects: – SubjectFull: Client/server computing management Type: general – SubjectFull: Resource allocation Type: general – SubjectFull: Cloud computing Type: general – SubjectFull: Distributed algorithms Type: general – SubjectFull: Pareto analysis Type: general Titles: – TitleFull: An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Khamse-Ashari, Jalal – PersonEntity: Name: NameFull: Lambadaris, Ioannis – PersonEntity: Name: NameFull: Kesidis, George – PersonEntity: Name: NameFull: Urgaonkar, Bhuvan – PersonEntity: Name: NameFull: Zhao, Yiqiang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: 12/1/2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 10459219 Numbering: – Type: volume Value: 29 – Type: issue Value: 12 Titles: – TitleFull: IEEE Transactions on Parallel & Distributed Systems Type: main |
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