Study of Effective Scheduling Algorithm for Application of Big Data

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Tanmay Paul
Tanmay Paul
α Maulana Abul Kalam Azad University of Technology, West Bengal Maulana Abul Kalam Azad University of Technology, West Bengal

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Study of Effective Scheduling Algorithm for Application of Big Data

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Abstract

In this new era with the advancement in the technological world the data storage, analysis becomes a major problem. Although the availability of different data storage component like electronic storage such as hard drive or virtual storage such as cloud still the problems remains. The major issue is processing the data because usually the data is in several format and size. Usually processing such huge amount of data with several formats can be time consuming. Using of application such as Hadoop can be beneficial but using of scheduling algorithm can be the best way to for data set analysis to make the process time efficient and analysis the requirement of different scheduling algorithm for the specific data set. In this paper we analysis different data set to explain the most effective scheduling algorithm for that specific data set and then store and execute data set after processing.

References

10 Cites in Article
  1. Youngho Song,; Young-Sung Shin,Miyoung Jang,Jae-Woo Chang (2017). Design and implementation of HDFS data encryption scheme using ARIA algorithm on Hadoop.
  2. E Tabak,B Cambazoglu,Cevdet Aykanat (2014). Improving the Performance of IndependentTask Assignment Heuristics MinMin,MaxMin and Sufferage.
  3. S Guia,A Espírito-Santo,; Paciello,F Abate,; Pietrosanto (2015). A comparison between FFT and MCT for period measurement with an ARM microcontroller.
  4. Xueyan Tang,Yusen Li,Runtian Ren,Wentong Cai (2016). On First Fit Bin Packing for Online Cloud Server Allocation.
  5. Saptarshi Bhowmik,Sudipa Biswas,Karan Vishwakarma,Subhankar Chattoraj (2016). An Efficient Load Balancing Approach in a Cloud Computing Platform.
  6. Jianhua Jiang,; Gaochao Xu,; Xiaohui Wei (2006). An Enhanced Data-aware Scheduling Algorithm for Batch-mode Data intensive Jobs on Data Grid.
  7. David Taylor-Fuller; Susan,J Lincke (2007). A QoS comparison of 4G first-come-first-serve load sharing algorithms involving speech & packet data.
  8. Swati Yadav,Santosh Vishwakarma,Ashok Verma (2015). Efficient & Accurate Scheduling Algorithm for Cloudera Hadoop.
  9. Jeroen Laverman,Dennis Grewe,Olaf Weinmann,Marco Wagner,Sebastian Schildt (2016). Integrating Vehicular Data into Smart Home IoT Systems Using Eclipse Vorto.
  10. J Monte,K Pattipati (2002). Scheduling parallelizable tasks to minimize make-span and weighted response time.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Tanmay Paul. 2017. \u201cStudy of Effective Scheduling Algorithm for Application of Big Data\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 17 (GJCST Volume 17 Issue C1): .

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Issue Cover
GJCST Volume 17 Issue C1
Pg. 37- 40
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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H.2.8
Version of record

v1.2

Issue date

April 26, 2017

Language
en
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In this new era with the advancement in the technological world the data storage, analysis becomes a major problem. Although the availability of different data storage component like electronic storage such as hard drive or virtual storage such as cloud still the problems remains. The major issue is processing the data because usually the data is in several format and size. Usually processing such huge amount of data with several formats can be time consuming. Using of application such as Hadoop can be beneficial but using of scheduling algorithm can be the best way to for data set analysis to make the process time efficient and analysis the requirement of different scheduling algorithm for the specific data set. In this paper we analysis different data set to explain the most effective scheduling algorithm for that specific data set and then store and execute data set after processing.

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Study of Effective Scheduling Algorithm for Application of Big Data

Tanmay Paul
Tanmay Paul Maulana Abul Kalam Azad University of Technology, West Bengal

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