Modelling and prediction of resources and services state evolvement for efficient runtime adaptations.
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| Title: | Modelling and prediction of resources and services state evolvement for efficient runtime adaptations. |
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| Authors: | Kyriazis, Dimosthenis1 dimos@unipi.gr |
| Source: | Future Generation Computer Systems. May2019, Vol. 94, p1-10. 10p. |
| Subjects: | Cloud computing, Prediction models, Distributed computing, Virtual machine systems, Polynomial approximation |
| Abstract: | Abstract Regardless of their implementation aspects and distribution elements, i.e. centralized or distributed, service-based environments such as cloud computing and edge/fog infrastructures, enable the provisioning of services addressing a wide range of application domains. The key requirement for users and consumers of such services refers to the corresponding levels of quality, which is affected both by the real-world dynamics – given the non-deterministic use of services, and by the underlying resources state – given the typically virtualized sharing nature of the resources. In this paper, an approach is presented that aims at estimating the evolvement of services and resources state in order to provide insights for runtime adaptations, as required to ensure services quality. The state refers to different metrics/parameters such as memory, number of users, throughput, etc, and can be extended and applied to different ones. The proposed approach exploits polynomial regression and prediction to identify the aforementioned state evolvement by mapping the two first monitoring data points for each metric/parameter to the corresponding function that depicts their evolvement. The latter provides added value in different cases, including among others the adaptation of monitoring time intervals, the estimation of the potential breach of quality thresholds, and the prediction of the time for runtime adaptations and scaling decisions. The effectiveness of the implemented approach is demonstrated and evaluated through a set of different scenarios. Highlights • Prediction of services and resources metrics based on polynomial approximation. • Estimation of time intervals for triggering runtime adaptations to provide quality guarantees. • Architecture with distributed decision points evaluated in a container-oriented setting. [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: 134793700 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Modelling and prediction of resources and services state evolvement for efficient runtime adaptations. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kyriazis%2C+Dimosthenis%22">Kyriazis, Dimosthenis</searchLink><relatesTo>1</relatesTo><i> dimos@unipi.gr</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Future+Generation+Computer+Systems%22">Future Generation Computer Systems</searchLink>. May2019, Vol. 94, p1-10. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+computing%22">Distributed computing</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+machine+systems%22">Virtual machine systems</searchLink><br /><searchLink fieldCode="DE" term="%22Polynomial+approximation%22">Polynomial approximation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Abstract Regardless of their implementation aspects and distribution elements, i.e. centralized or distributed, service-based environments such as cloud computing and edge/fog infrastructures, enable the provisioning of services addressing a wide range of application domains. The key requirement for users and consumers of such services refers to the corresponding levels of quality, which is affected both by the real-world dynamics – given the non-deterministic use of services, and by the underlying resources state – given the typically virtualized sharing nature of the resources. In this paper, an approach is presented that aims at estimating the evolvement of services and resources state in order to provide insights for runtime adaptations, as required to ensure services quality. The state refers to different metrics/parameters such as memory, number of users, throughput, etc, and can be extended and applied to different ones. The proposed approach exploits polynomial regression and prediction to identify the aforementioned state evolvement by mapping the two first monitoring data points for each metric/parameter to the corresponding function that depicts their evolvement. The latter provides added value in different cases, including among others the adaptation of monitoring time intervals, the estimation of the potential breach of quality thresholds, and the prediction of the time for runtime adaptations and scaling decisions. The effectiveness of the implemented approach is demonstrated and evaluated through a set of different scenarios. Highlights • Prediction of services and resources metrics based on polynomial approximation. • Estimation of time intervals for triggering runtime adaptations to provide quality guarantees. • Architecture with distributed decision points evaluated in a container-oriented setting. [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.2018.09.035 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Cloud computing Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Distributed computing Type: general – SubjectFull: Virtual machine systems Type: general – SubjectFull: Polynomial approximation Type: general Titles: – TitleFull: Modelling and prediction of resources and services state evolvement for efficient runtime adaptations. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kyriazis, Dimosthenis IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: May2019 Type: published Y: 2019 Identifiers: – Type: issn-print Value: 0167739X Numbering: – Type: volume Value: 94 Titles: – TitleFull: Future Generation Computer Systems Type: main |
| ResultId | 1 |
