Cloud computing-based resource provisioning using k-means clustering and GWO prioritization

Meenakshi, A.; Sirmathi, H.; Ruth, J. Anitha

doi:10.1007/s00500-018-3632-9

Cited by 14 publications

(9 citation statements)

References 20 publications

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“…Traditional data processing models have fallen into a bottleneck under massive, high-dimensional data with the rise of cloud computing technology (Rafique et al, 2021;Meenakshi et al, 2019). Cloud computing is applied to process massive data in a short time on the distributed parallel computing basis, and has strong network service ability (Kim and Jeong, 2017).…”

Section: A Cloud Load Forecasting Model With Nonlinear Changes Using Whale Optimization Algorithm Hybrid Strategy -Extreme Learning Machimentioning

confidence: 99%

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

Peng¹,

Wen²,

Tseng

et al. 2021

Preprint

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This study proposes a novel cloud load prediction model and combines hybrid whale optimizer (HWOA) and extreme learning machine (ELM) together for strong nonlinear mapping ability. Accurate cloud load prediction improves the cloud service efficiency and serves as the foundation for network scheme due to traditional linear forecasting models are unable to predict cloud computing resources with nonlinear changes on massive multiplication and cloud computing data complexity, effectively. The proposed cloud load forecasting model is to employ HWOA optimizer to optimize the ELM model random parameters. The contributions of this study are as follows. (1) the HWOA optimizer is to solve the whale optimizer local extremum problem; (2) the proposed HWOA optimizer reduces the ELM random parameters on cloud load forecasting; (3) the convergence performance verifies the benchmark testing functions; and (4) three simulation experiments are conducted to test the cloud load forecast effect. The result indicated that the convergence analysis reveals the HWOA optimizer outperforms the prior optimizers. The proposed cloud load prediction model obtains better forecasting results. The mean absolute percentage error and root mean square error of the proposed model are less than 14% and 11, respectively. Accurate cloud load forecasting lays a foundation for effective deployment of cloud computing resources and maximization of economic benefits.

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Section: A Cloud Load Forecasting Model With Nonlinear Changes Using Whale Optimization Algorithm Hybrid Strategy -Extreme Learning Machimentioning

confidence: 99%

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

Peng¹,

Wen²,

Tseng

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…They also proposed an approach that offers time series analysis for rejuvenation scheduling in an attempt to reduce the fault time by predicting the appropriate time for rejuvenation. Meenakshi et al [15] suggested another approach that allocated resources with minimal waste in cloud computing based on sending requests to the request tuner.…”

Section: Literature Reviewmentioning

confidence: 99%

A fuzzy neural network for web service selection aimed at dynamic software rejuvenation

Kalantari¹,

Ebrahimnejad²,

Motameni³

2020

Turk J Elec Eng & Comp Sci

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Software rejuvenation is an effective technique to counteract software aging in continuously running applications such as web service-based systems. In these systems, web services are allocated based on the requirements of receivers and the facilities of servers. One of the challenges while assigning web services is how to select appropriate server to reduce faults. In this paper, we propose dynamic software rejuvenation as a proactive fault-tolerance technique based on the neural fuzzy system. While considering a threshold for the rejuvenation of each web service, we completed the training based on the features of the service providers as well as the requirements of the receivers. The results of simulations revealed that our strategy can decrease the failure rate in comparison with state-of-the-art strategies and improve system availability in web services.

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“…Moreover, they set forth an approach that offers a timeseries analysis for rejuvenation scheduling in an attempt to mitigate the fault time by predicting the appropriate time for rejuvenation. Meenakshi et al [14] suggested another approach that allocated resources with minimal waste in cloud computing by sending requests to the request tuner.…”

Section: Literature Reviewmentioning

confidence: 99%

Presenting a new fuzzy system for web service selection aimed at dynamic software rejuvenation

Kalantari

Ebrahimnejad

Motameni

2020

Complex Intell. Syst.

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As an effective technique to counteract software aging, software rejuvenation is applied in continuously running applications such as web service-based systems. In such systems, web services are allocated depending on the requirements of receivers and the facilities of servers. One of the challenging issues during assignment of web services is how to select the appropriate server to minimize faults. In this paper, we proposed dynamic software rejuvenation in the form of a proactive fault-tolerance technique based on fuzzy system. While including a threshold for the rejuvenation of each web service, we carried out the training phase based on the features of the service providers as well as the receivers’ requirements. The results of simulations revealed that our strategy can mitigate the failure rate of web services by 45, 40, 23, and 12% in comparison with the non-fuzzy, regression-based, Markov-based, and ACOGELS-based web service rejuvenation strategies, respectively.

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Cloud computing-based resource provisioning using k-means clustering and GWO prioritization

Cited by 14 publications

References 20 publications

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

A Cloud Load Forecasting Model with Nonlinear Changes using Whale Optimization Algorithm Hybrid Strategy - extreme Learning Machine

A fuzzy neural network for web service selection aimed at dynamic software rejuvenation

Presenting a new fuzzy system for web service selection aimed at dynamic software rejuvenation

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