Detailed Cloud Linear Regression Services in Cloud Computing Environment

Omer K. Jasim Mohammad, Mohammed E. Seno, Ban N. Dhannoon

Abstract


This paper presents a novel cloud-based machine learning framework centered around a linear regression method known as Cloud Linear Regression (CLR). CLR combines elements of cloud technology and machine learning principles. Furthermore, it explores the connection between cloud task scheduling, distribution, and machine learning methodologies, showcasing how linear regression techniques play a pivotal role in enhancing the cloud environment. CLR demonstrated it is effectiveness in dealing with expansive environments that have big data by exhibiting high thorough mining for the best resource predictive accuracy and response times, it has been applied to three scenarios for the best CPU accuracy utilization of the prediction which was (45 %), (53.44 %), and (59.81%) respectively. Moreover, CLR offers an efficient remedy for managing resources, including task scheduling, provisioning, allocation, and ensuring availability. CLR obtained the highest performance of (40%) with multitasking resources, (72%) with Memory utilization, (90% with logical Disk utilization, and (30 %) with Bandwidth utilization.


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References


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DOI: https://doi.org/10.31449/inf.v48i12.6771

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