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Tricube Weighted Linear Regression and Interquartile for Cloud Infrastructural Resource Optimization

Neema George1,*, B. K. Anoop1, Vinodh P. Vijayan2

1 Srinivas University, Mangalore, India
2 Mangalam College of Engineering, Kottayam, India

* Corresponding Author: Neema George. Email:

Computer Systems Science and Engineering 2023, 45(3), 2281-2297.


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.


Cite This Article

N. George, B. K. Anoop and V. P. Vijayan, "Tricube weighted linear regression and interquartile for cloud infrastructural resource optimization," Computer Systems Science and Engineering, vol. 45, no.3, pp. 2281–2297, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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