CLR: Cloud Linear Regression Environment as a More Effective Resource-Task Scheduling Environment (State-of-the-Art)

CLR: Cloud Linear Regression Environment as a More Effective Resource-Task Scheduling Environment (State-of-the-Art)

Task scheduling, Resource Allocation, Machin Learning Algorithms, linear regression, cloud-host, VM-placement, VM-migration.

The cloud paradigm has swiftly developed, and it is now well known as one of the emerging technologies that will have a significant influence on technology and society in the next few years. Cloud computing also has several benefits, including lower operating costs, server consolidation, flexible system setup, and elastic resource supply. However, there are still technological hurdles to overcome, particularly with real-time applications by providing resources. Resources allocation management most charming part of cloud computing; therefore, several authors have worked in the area of resource usage. This study introduces an innovative cloud machine learning framework-based linear regression approach called cloud linear regression (CLR), which entails both cloud technology and machine learning concept. CLR using machine learning yielded good prediction results for resource allocation management, as appeared with many researching, and still seek, research to raise optimal solutions to the resources' allocation problem as the aim of this study.   This study discusses the relation between cloud resource allocation management and machine learning techniques by illustrating the role of linear regression methods, resource distribution, and task scheduling. The analytical analysis shows that the CLR promises to present an effective solution for resources (scheduling, provisioning, allocation, and availability).

Seno, Mohammed E.

Mohammad, Omer K. Jasim

Dhannoon, Ban N.

International Journal of Interactive Mobile Technologies (iJIM); Vol. 16 No. 22 (2022); pp. 157-175

1865-7923

International Association of Online Engineering (IAOE), Vienna, Austria

2022-11-29

Copyright (c) 2022 Haider TH.Salim ALRikabi; Mohammed E. Seno, Omer K. Jasim Mohammad, Ban N. Dhannoon

https://creativecommons.org/licenses/by/4.0

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eng

info:eu-repo/semantics/article

info:eu-repo/semantics/publishedVersion

Peer-reviewed Article