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Performance evaluation of IaaS cloud using Stochastic Neural Network

Published: 01 January 2022 Publication History

Abstract

Cloud computing is an on-demand model that computes shared and dynamic resource availability in a remote or independent location. Cloud computing provides many services online to clients in a pay-as-you-go manner. Nowadays, many organizations use cloud computing techniques with the prime motive that cost can be reduced, and resources are dynamically allocated. Performance evaluation and measurement approaches for cloud computing help the cloud services consumer to evaluate their cloud system based on performance attributes. Although the researchers have proposed many techniques and approaches in this direction in past decades, none of them has attained widespread industrial benefit. This paper proposes a novel quality evaluation methodology named Stochastic Neural Net (SNN) to evaluate the cloud quality of Infrastructure as a Service (IaaS). This model deeply measures the performance by considering every activity of the IaaS system. Based on their characteristics, these works suggest key QoS factors for individual parts and activities. The individual QoS metric makes the SNN methodology acquire accurate results regarding performance measurement. The performance evaluation result can be used to improve the cloud computing system. The proposed model is compared with other standard models. The experimental comparison shows that the proposed model is more efficient than other standard models.

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  • (2024)A task offloading strategy considering forwarding errors based on cloud–fog collaborationCluster Computing10.1007/s10586-024-04439-x27:6(8531-8555)Online publication date: 1-Sep-2024
  • (2023)Cost-efficient resource scheduling in cloud for big data processing using metaheuristic search black widow optimization (MS-BWO) algorithmJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22204844:3(4397-4417)Online publication date: 1-Jan-2023

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    Published In

    cover image Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
    Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology  Volume 43, Issue 4
    2022
    1429 pages

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    IOS Press

    Netherlands

    Publication History

    Published: 01 January 2022

    Author Tags

    1. IaaS
    2. stochastic model
    3. performance measure
    4. neural network
    5. availability

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    View all
    • (2024)A task offloading strategy considering forwarding errors based on cloud–fog collaborationCluster Computing10.1007/s10586-024-04439-x27:6(8531-8555)Online publication date: 1-Sep-2024
    • (2023)Cost-efficient resource scheduling in cloud for big data processing using metaheuristic search black widow optimization (MS-BWO) algorithmJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-22204844:3(4397-4417)Online publication date: 1-Jan-2023

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