Detailed Cloud Linear Regression Services in Cloud Computing Environment

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 its 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. Such obtained results discovered in this form due to type of cloud infrastructure style (virtually environment). 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. © 2024 Slovene Society Informatika. All rights reserved.