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Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization.

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Title: Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization.
Authors: George, Neema1 neemageo165@gmail.com, Anoop, B. K.1, Vijayan, Vinodh P.2
Source: Computer Systems Science & Engineering. 2023, Vol. 45 Issue 3, p2281-2297. 17p.
Subjects: Cloud computing, Workload of computer networks, Quality of service, Central processing units, Virtual machine systems
Abstract: Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application. When workload execution, accuracy, and cost are accurately stabilized in opposition to the best possible framework in real-time, efficiency is attained. In addition, every workload or application required for the framework is characteristic and these essentials change over time. But, the existing method was failed to ensure the high Quality of Service (QoS). In order to address this issue, a Tricube Weighted Linear Regression-based Inter Quartile (TWLR-IQ) for Cloud Infrastructural Resource Optimization is introduced. A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources (i.e., CPU, RAM, and network bandwidth utilization) based on the usage history in each cloud server. Then, Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration. Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics. The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine (VM) Migrations as compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
Copyright of Computer Systems Science & Engineering is the property of Tech Science Press 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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DbLabel: Engineering Source
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PubType: Academic Journal
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  Data: Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization.
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  Data: <searchLink fieldCode="AR" term="%22George%2C+Neema%22">George, Neema</searchLink><relatesTo>1</relatesTo><i> neemageo165@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Anoop%2C+B%2E+K%2E%22">Anoop, B. K.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Vijayan%2C+Vinodh+P%2E%22">Vijayan, Vinodh P.</searchLink><relatesTo>2</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Computer+Systems+Science+%26+Engineering%22">Computer Systems Science & Engineering</searchLink>. 2023, Vol. 45 Issue 3, p2281-2297. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Cloud+computing%22">Cloud computing</searchLink><br /><searchLink fieldCode="DE" term="%22Workload+of+computer+networks%22">Workload of computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22Quality+of+service%22">Quality of service</searchLink><br /><searchLink fieldCode="DE" term="%22Central+processing+units%22">Central processing units</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+machine+systems%22">Virtual machine systems</searchLink>
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  Data: Cloud infrastructural resource optimization is the process of precisely selecting the allocating the correct resources either to a workload or application. When workload execution, accuracy, and cost are accurately stabilized in opposition to the best possible framework in real-time, efficiency is attained. In addition, every workload or application required for the framework is characteristic and these essentials change over time. But, the existing method was failed to ensure the high Quality of Service (QoS). In order to address this issue, a Tricube Weighted Linear Regression-based Inter Quartile (TWLR-IQ) for Cloud Infrastructural Resource Optimization is introduced. A Tricube Weighted Linear Regression is presented in the proposed method to estimate the resources (i.e., CPU, RAM, and network bandwidth utilization) based on the usage history in each cloud server. Then, Inter Quartile Range is applied to efficiently predict the overload hosts for ensuring a smooth migration. Experimental results show that our proposed method is better than the approach in Cloudsim under various performance metrics. The results clearly showed that the proposed method can reduce the energy consumption and provide a high level of commitment with ensuring the minimum number of Virtual Machine (VM) Migrations as compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Computer Systems Science & Engineering is the property of Tech Science Press 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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    Identifiers:
      – Type: doi
        Value: 10.32604/csse.2023.028117
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 17
        StartPage: 2281
    Subjects:
      – SubjectFull: Cloud computing
        Type: general
      – SubjectFull: Workload of computer networks
        Type: general
      – SubjectFull: Quality of service
        Type: general
      – SubjectFull: Central processing units
        Type: general
      – SubjectFull: Virtual machine systems
        Type: general
    Titles:
      – TitleFull: Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization.
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            NameFull: George, Neema
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            NameFull: Anoop, B. K.
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            NameFull: Vijayan, Vinodh P.
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            – D: 01
              M: 06
              Text: 2023
              Type: published
              Y: 2023
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              Value: 45
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            – TitleFull: Computer Systems Science & Engineering
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