|An Investigation Study on QoS and Traffic Aware Job Scheduling Techniques with Big Data|
|C.R. Durga Devi1 , R. Manicka Chezian2|
1 Department of Computer Science, NGM College, Pollachi, India.
2 Department of Computer Science, NGM College, Pollachi, India.
|Correspondence should be addressed to: firstname.lastname@example.org.|
Section:Research Paper, Product Type: Journal Paper
Volume-5 , Issue-11 , Page no. 1-7, Nov-2017
Online published on Nov 30, 2017
Copyright © C.R. Durga Devi, R. Manicka Chezian . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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IEEE Style Citation: C.R. Durga Devi, R. Manicka Chezian, “An Investigation Study on QoS and Traffic Aware Job Scheduling Techniques with Big Data”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.1-7, 2017.
MLA Style Citation: C.R. Durga Devi, R. Manicka Chezian "An Investigation Study on QoS and Traffic Aware Job Scheduling Techniques with Big Data." International Journal of Computer Sciences and Engineering 5.11 (2017): 1-7.
APA Style Citation: C.R. Durga Devi, R. Manicka Chezian, (2017). An Investigation Study on QoS and Traffic Aware Job Scheduling Techniques with Big Data. International Journal of Computer Sciences and Engineering, 5(11), 1-7.
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|Big Data Analytics (BDA) applications are new software application for processing large amount of data to collect the hidden value. Big data is defined as a datasets whose size is beyond the capability of usual relational databases to collect, direct and handle the data with lesser latency. Most of the recent research works aimed to reduce the traffic and workload for convalescing the quality of services in big data. In recent times, many research works are carried out for getting better the performance of regression and classification process during the data access from the big data. However, the job completion time and memory space complexity remained challenging issue. Our main objective is to reduce the space complexity and time complexity during the data accessing from big data. In order to reduce the job completion time and memory space consumption, many existing techniques are reviewed. The key objective of the research is to increase performance of traffic aware job scheduling techniques with minimal space and time complexity. In this paper, review of various existing job scheduling techniques is carried out. The study and analysis about the performance of three existing techniques in terms of their space and time complexity is measured as the number of user requests increases and a comparison of the results between these techniques is carried out. Limitations of existing techniques are also discussed.|
|Key-Words / Index Term :|
|Big Data Analytics, Relational databases, Quality of services, Regression, Classification|
 Peng Li, Song Guo, Toshiaki Miyazaki, Xiaofei Liao, Hai Jin, Albert Y. Zomaya,Kun Wang, “Traffic-aware Geo-distributed Big Data Analytics with Predictable Job Completion Time”, IEEE Transactions on Parallel and Distributed Systems, Volume 28, Issue 6, Pages 1785 – 1796, 2017.
 Qinghua Lu, Shanshan Li, Weishan Zhang and Lei Zhang, “A Genetic Algorithm-Based Job Scheduling Model for Big Data Analytics”, EURASIP Journal on Wireless Communications and Networking, Springer, Volume 16, Issue 152, Pages 1-9, 2016
 Rajinder Sandhu and Sandeep K. Sood, “Scheduling Of Big Data Applications on Distributed Cloud Based on Qos Parameters”, Cluster Computing, Springer, Volume 18, Issue 2, Pages 817–828, June 2015
 Zhen Jia, Jianfeng Zhan, Lei Wang, Chunjie Luo, Wanling Gao, Yi Jin, Rui Han and Lixin Zhang, “Understanding Big Data Analytics Workloads on Modern Processors”, IEEE Transactions on Parallel and Distributed Systems, Volume 28, Issue 6, Pages 1797 – 1810, June 2017
 Jun-Sung Kim, Kyu-Young Whang, Hyuk-Yoon Kwon and Il-Yeol Song, “PARADISE: Big Data Analytics using the DBMS Tightly Integrated with the Distributed File System”, World Wide Web, Springer, Volume 19, Issue 3, , Pages 299–322, May 2016
 Bogdan Nicolae, Carlos H. A. Costay, Claudia Misalez, Kostas Katrinis and Yoonho Park, “Leveraging Adaptive I/O to Optimize Collective Data Shuffling Patterns for Big Data Analytics”, IEEE Transactions on Parallel and Distributed Systems, Volume 28, Issue 6, Pages 1663 – 1674, June 2017
 Carlos Ordonez, Yiqun Zhang and Wellington Cabrera, “The Gamma Matrix to Summarize Dense and Sparse Data Sets for Big Data Analytics”, IEEE Transactions on Knowledge and Data Engineering, Volume 28, Issue 7, Pages 1905 – 1918 July 2016,
 Kun Wang, Huining Li, Yixiong Feng, and Guangdong Tian, “Big Data Analytics for System Stability Evaluation Strategy in the Energy Internet”, IEEE Transactions on Industrial Informatics, Volume 13, Issue 4, Pages 1969 – 1978, August 2017
 L. U. Laboshin, A. A. Lukashin and V. S. Zaborovsky, “The Big Data Approach to Collecting and Analyzing Traffic Data in Large Scale Networks”, Procedia Computer Science, Elsevier, Volume 103, Pages 536-542, 2017.
 Mohd Usama, Mengchen Liu and Min Chen, “Job schedulers for Big data processing in Hadoop environment: Testing real-life schedulers using benchmark programs”, Digital Communications and Networks, Elsevier, Pages 1-14, August 2017.
 E. Sivaraman, Dr.R.Manickachezian, “High Performance and Fault Tolerant Distributed File System for Big Data Storage and Processing Using Hadoop”, IEEE Xplore Digital Library, DOI: 10.1109/ICICA.2014.16, E-ISBN: 978-1-4799-3966-4
 Sapinderjit Kaur, Kirandeep Kaur, Amit.Chhabra, “Parallel Job Scheduling Using Grey Wolf Optimization Algorithm For Heterogenous Multi-Cluster Environment”, International Journal of Computer Science and Engineering Vol.5 , Issue.10 , pp.44-53, Oct-2017
 S.Hemalatha, Dr.R.Manickachezian, “Implicit Security Architecture Framework in Cloud Computing Based on Data Partitioning and Security Key Distribution”, International Journal of Emerging Technologies in Computational and Applied Sciences, pp. 76-81, ISSN: 2279-0055, Feb. 2013.