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Fog Computing Analysis Based on Internet of Thing: A Review Hindreen Rashid Abdulqadir & Nawzat Sadiq Ahmed

Fog Computing Analysis Based on Internet of Thing: A Review

Author (s)

Hindreen Rashid Abdulqadir & Nawzat Sadiq Ahmed

Abstract

­Cloud machine architectures face many drawbacks due to the improved autonomous and distributed IoT configuration. The IoT is closer to the cloud infrastructure. The fog offers IoT data care and storage locally on IoT devices rather than in the cloud. The fog provides quicker responses and better performance in relation to the cloud. The best alternative for IoT to provide powerful and effective resources for many IoT customers may therefore be called Fog Computing. This paper aims at fog computing’s state-of-the-art and alignment with IoT in detailing the advantages and challenges of implementation. This study will concentrate also on the conception of cloud and fog technology and the application of the cloud and fog paradigm to improve modern IoT technologies. Finally, open issues and alternative research directions are discussed on fog estimation and IoT.

 Keywords: Internet of Things (IoT) , Big data ,  Analyses, Fog Computing , Cloud computing.

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Title: Fog Computing Analysis Based on Internet of Thing: A Review
Author: Hindreen Rashid Abdulqadir & Nawzat Sadiq Ahmed
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4534437
Media: Online
Volume: 5
Issue: 3
Acceptance Date: 06/02/2021
Date of Publication: 11/02/2021
PDF URL: https://ijsab.com/wp-content/uploads/693.pdf
Free download: Available
Page: 137-147
First Page: 137
Last Page: 147
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Abdulqadir, H. R. & Ahmed, N. S. (2021). Fog computing Analysis Based on Internet of Thing: A Review. International Journal of Science and Business, 5(3), 137-147. doi: https://doi.org/10.5281/zenodo.4534437

 

About Author (s)

Hindreen Rashid Abdulqadir  (corresponding author),   Information Technology Department,  Akre Technical College of Informatics, Duhok Polytechnic University, Duhok Kurdistan Region, Iraq. Email: Hindreen.rashid@dpu.edu.krd

Nawzat Sadiq Ahmed, Information Technology Management Department, Technical College of Administration, Duhok Polytechnic University,Duhok, Kurdistan Region, Iraq. Email: nawzat.ahmed@dpu.edu.krd

 

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DOI: https://doi.org/10.5281/zenodo.4534437

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Big Data Analysis for Data Visualization: A Review Zhwan M. Khalid, Subhi R. M. Zeebaree

Big Data Analysis for Data Visualization: A Review

Author (s)

Zhwan M. Khalid, Subhi R. M. Zeebaree

Abstract

­One of the main characteristics of scaling data is complexity. Heterogeneous data contributes to data integration and the process of big data problems. Both of them are essential and difficult to visualize and interpret large-scale databases since they require considerable data processing and storage capacity. The data age, where data grows exponentially, is a significant struggle to extract data in a manner that the human mind can grasp. This paper reviews and provides data visualization and the Heterogeneous Distributed Storage description and their challenges using different methods through some previous researches. Besides, the results of reviewed research works are compared, and the fundamental shift in the world of large data visualization of virtual reality is discussed.

 Keywords: Big Data, Heterogeneous, Visualization, Distribute Data Value.

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Title: Big Data Analysis for Data Visualization: A Review
Author: Zhwan M. Khalid, Subhi R. M. Zeebaree
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4481357
Media: Online
Volume: 5
Issue: 2
Acceptance Date: 20/01/2021
Date of Publication: 25/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/671.pdf
Free download: Available
Page: 64-75
First Page: 64
Last Page: 75
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Khalid, Z. M. and Zeebaree, S. R. M. (2021). Big Data Analysis for Data Visualization: A Review. International Journal of Science and Business, 5(2), 64-75. doi: https://doi.org/10.5281/zenodo.4481357

Retrieved from https://ijsab.com/wp-content/uploads/671.pdf

 

About Author (s)

Zhwan M. Khalid (corresponding author), Information System Engineering Dept.,  Erbil polytechnic University, KRG-Iraq. eng.zhwan90@uor.edu.krd

Subhi R.M Zeebaree, Duhok Polytechnic University, KRG-Iraq. subhi.rafeeq@dpu.edu.krd

 

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DOI: https://doi.org/10.5281/zenodo.4481357

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Improvised Distributions framework of Hadoop: A review Baydaa Hassan Husain & Subhi R. M. Zeebaree

Improvised Distributions framework of Hadoop: A review

Author (s)

Baydaa Hassan Husain & Subhi R. M. Zeebaree

Abstract

­HADOOP is an open-source virtualization technology that allows the distributed processing of large data sets across standardized server clusters. With two modules, HADOOP Distributed File System (HDFS) and MapReduce framework, it is designed to scale single servers to thousands of computers, providing local computation and storage. Over a decade after HADOOP emerged on the forefront as an open system for Big Data analysis. Its growth has prompted several improvisations for particular data processing needs, based on the type of processing conditions at various periods of computation. This paper, through reviewing several kinds of research provides the basic HADOOP system structure and the description of the MapReduce, HDFS Efficiency. Explaining how the HADOOP framework can overcome the “5Vs” challenges in Big Data. However, in addition to the many benefits of the HADOOP system, like fault tolerance, reliability, high availability, scalable, decreases execution time, reduces latency, improve the security issues, improving the quality of data analysis, better scheduling model, and cost-efficiently. On the other hand, there were some barriers and challenges regarding adjusting data regularly, security issues, and load balancing. Finally, the certainly benefit and challenges of the HADOOP system have been represented paving the way for the future research to find solutions to these challenges.

 Keywords: HADOOP, HDFS, MapReduce, Big Data.

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Title: Improvised Distributions framework of Hadoop: A review
Author: Baydaa Hassan Husain & Subhi R. M. Zeebaree
Journal Name: International Journal of Science and Business
Website: ijsab.com
ISSN: ISSN 2520-4750 (Online), ISSN 2521-3040 (Print)
DOI: https://doi.org/10.5281/zenodo.4461761
Media: Online
Volume: 5
Issue: 2
Acceptance Date: 20/01/2021
Date of Publication: 25/01/2021
PDF URL: https://ijsab.com/wp-content/uploads/668.pdf
Free download: Available
Page: 31-41
First Page: 31
Last Page: 41
Paper Type: Literature Review
Current Status: Published

 

Cite This Article:

Baydaa Hassan Husain & Subhi R. M. Zeebare (2021). Improvised Distributions framework of Hadoop: A review. International Journal of Science and Business, 5(2), 31-41. doi: https://doi.org/10.5281/zenodo.4461761

Retrieved from https://ijsab.com/wp-content/uploads/668.pdf

 

About Author (s)

Baydaa Hassan Husain, ISE Department, Erbil Polytechnic University, Erbil – Kurdistan Region – Iraq, Baydaa.mei20@epu.edu.iq.

Subhi R. M. Zeebaree (corresponding author), Information Technology Department, Duhok Polytechnic University, Duhok – Kurdistan Region – Iraq, subhi.rafeeq@dpu.edu.krd.

 

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DOI: https://doi.org/10.5281/zenodo.4461761