Making mapreduce scheduling effective in erasure-coded storage clusters

An efficient MapReduce scheduling scheme for processing large multimedia data

Multimedia Tools and Applications ◽

10.1007/s11042-016-4026-6 ◽

2016 ◽

Vol 76 (16) ◽

pp. 17273-17296 ◽

Cited By ~ 5

Author(s):

Kyoungsoo Bok ◽

Jaemin Hwang ◽

Jongtae Lim ◽

Yeonwoo Kim ◽

Jaesoo Yoo

Keyword(s):

Multimedia Data ◽

Scheduling Scheme ◽

Mapreduce Scheduling

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Probabilistic Network-Aware Task Placement for MapReduce Scheduling

2016 IEEE International Conference on Cluster Computing (CLUSTER) ◽

10.1109/cluster.2016.48 ◽

2016 ◽

Cited By ~ 14

Author(s):

Haiying Shen ◽

Ankur Sarker ◽

Lei Yu ◽

Feng Deng

Keyword(s):

Probabilistic Network ◽

Task Placement ◽

Mapreduce Scheduling

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Reducing Stage Weight Estimation Error of Slow Task Detection in MapReduce Scheduling

The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) - Advances in Intelligent Systems and Computing ◽

10.1007/978-3-319-74690-6_28 ◽

2018 ◽

pp. 284-291 ◽

Cited By ~ 1

Author(s):

Utsav Upadhyay ◽

Geeta Sikka

Keyword(s):

Estimation Error ◽

Weight Estimation ◽

Mapreduce Scheduling

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SPO: A Secure and Performance-aware Optimization for MapReduce Scheduling

Journal of Network and Computer Applications ◽

10.1016/j.jnca.2020.102944 ◽

2021 ◽

Vol 176 ◽

pp. 102944

Author(s):

Neda Maleki ◽

Amir Masoud Rahmani ◽

Mauro Conti

Keyword(s):

And Performance ◽

Mapreduce Scheduling

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Analysis of hadoop MapReduce scheduling in heterogeneous environment

Ain Shams Engineering Journal ◽

10.1016/j.asej.2020.06.009 ◽

2020 ◽

Author(s):

Khushboo Kalia ◽

Neeraj Gupta

Keyword(s):

Heterogeneous Environment ◽

Hadoop Mapreduce ◽

Mapreduce Scheduling

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A heuristic method towards deadline-aware energy-efficient mapreduce scheduling problem in Hadoop YARN

Cluster Computing ◽

10.1007/s10586-020-03146-7 ◽

2020 ◽

Author(s):

Vaibhav Pandey ◽

Poonam Saini

Keyword(s):

Energy Efficient ◽

Heuristic Method ◽

Scheduling Problem ◽

Mapreduce Scheduling

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Optimizing MapReduce scheduling using datanode load prediction

2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO) ◽

10.1109/eesco.2015.7253840 ◽

2015 ◽

Author(s):

Dharmesh Patel ◽

Mosin Hasan ◽

Kirti Sharma

Keyword(s):

Load Prediction ◽

Mapreduce Scheduling

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MapReduce Scheduling for Deadline-Constrained Jobs in Heterogeneous Cloud Computing Systems

IEEE Transactions on Cloud Computing ◽

10.1109/tcc.2015.2474403 ◽

2018 ◽

Vol 6 (1) ◽

pp. 127-140 ◽

Cited By ~ 22

Author(s):

Chien-Hung Chen ◽

Jenn-Wei Lin ◽

Sy-Yen Kuo

Keyword(s):

Cloud Computing ◽

Computing Systems ◽

Heterogeneous Cloud ◽

Mapreduce Scheduling

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Online MapReduce scheduling problem of minimizing the makespan

Journal of Combinatorial Optimization ◽

10.1007/s10878-015-9982-7 ◽

2015 ◽

Vol 33 (2) ◽

pp. 590-608 ◽

Cited By ~ 7

Author(s):

Cong Chen ◽

Yinfeng Xu ◽

Yuqing Zhu ◽

Chengyu Sun

Keyword(s):

Scheduling Problem ◽

Mapreduce Scheduling

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Survey on Various MapReduce Scheduling Algorithms

Handbook of Research on Cloud Computing and Big Data Applications in IoT - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-8407-0.ch022 ◽

2019 ◽

pp. 499-515

Author(s):

Vaibhav Pandey ◽

Poonam Saini

Keyword(s):

Social Networking ◽

Programming Model ◽

Scheduling Algorithms ◽

Optimal Decision ◽

Quality Requirements ◽

Hadoop Mapreduce ◽

Optimal Decision Making ◽

Mapreduce Scheduling

The advent of social networking and internet of things (IoT) has resulted in exponential growth of data in the last few years. This, in turn, has increased the need to process and analyze such data for optimal decision making. In order to achieve better results, there is an emergence of newly-built architectures for parallel processing. Hadoop MapReduce (MR) is a programming model that is considered as one of the most powerful computation tools for processing the data on a given cluster of commodity nodes. However, the management of clusters along with various quality requirements necessitates the use of efficient MR scheduling. The chapter discusses the classification of MR scheduling algorithms based on their applicability with required parameters of quality of service (QoS). After classification, a detailed study of MR schedulers has been presented along with their comparison on various parameters.

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