A brief review of scheduling algorithms of Map Reduce model using Hadoop

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-45 Number-1
Year of Publication : 2017
Authors : Adhishtha Tyagi, Sonia Sharma
DOI :  10.14445/22315381/IJETT-V45P209


Adhishtha Tyagi, Sonia Sharma " A brief review of scheduling algorithms of Map Reduce model using Hadoop", International Journal of Engineering Trends and Technology (IJETT), V45(1),37-42 March 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group

Scheduling has been an active area of research in computing systems since their inception. Hadoop framework has become very much popular and most widely used in distributed data processing. Hadoop has become a central platform to store big data through its Hadoop Distributed File System (HDFS) as well as to run analytics on this stored big data using its MapReduce component. The main objective is to study MapReduce framework, MapReduce model, scheduling in hadoop, various scheduling algorithms and various optimization techniques in job scheduling. Scheduling algorithms of MapReduce model using hadoop vary with design and behaviour, and are used for handling many issues like data locality, awareness with resource, energy and time.


[1] Qutaibah Althebyan , Omar ALQudah, Yaser Jararweh, Qussai Yaseen, “Multi-Threading Based Map Reduce Tasks Scheduling”, 5th International Conference on Information and Communication Systems (ICICS), 2014.
[2] YongLiang Xu, Wentong Cai, “Hadoop Job Scheduling with Dynamic Task Splitting”, International Conference on Cloud Computing Research and Innovation, 2015.
[3] Chen He, Ying Lu, David Swanson, “Real-Time Scheduling in Map Reduce Clusters”, IEEE International Conference on High Performance Computing and Communications & IEEE International Conference on Embedded and Ubiquitous Computing, 2013.
[4] Divya M., Annappa B., “Workload Characteristics and Resource Aware Hadoop Scheduler”, IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), 2015.
[5] Kamal Kc, Kemafor Anyanwu, “Scheduling Hadoop Jobs to Meet Deadlines”, 2nd IEEE International Conference on Cloud Computing Technology and Science, 2010.
[6] Li Liu, Yuan Zhou, Ming Liu, Guandong Xu, Xiwei Chen, Dangping Fan, Qianru Wang, “Preemptive Hadoop Jobs Scheduling under a Deadline”, Eighth International Conference on Semantics, Knowledge and Grids, 2012.
[7] Xiangming Dai, Brahim Bensaou, “Scheduling for response time in Hadoop MapReduce”, IEEE ICC 2016 SAC Cloud Communications and Networking, 2016.
[8] Qinghua Lu, Shanshan Li, Weishan Zhang, “Genetic Algorithms based Job Scheduling for Big Data Analytics”, International Conference on Identification, Information, and Knowledge in the Internet of Things, 2015.
[9] Ke Wang, Ning Liu, Iman Sadooghi, Xi Yang, Xiaobing Zhou, Tonglin Li, Michael Lang, Xian-He Sun, Ioan Raicu, “Overcoming Hadoop Scaling Limitations through Distributed Task Execution”, IEEE International Conference on Cluster Computing, 2015.
[10] Hong Mao, Shengqiu Hu, Zhenzhong Zhang, Limin Xiao, Li Ruan, “A Load-Driven Task Scheduler with Adaptive DSC for MapReduce” , IEEE/ACM International Conference on Green Computing and Communications, 2011.

Big Data, MapReduce and Hadoop framework, MapReduce model