New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing <scp>‐</scp> dominant resource fairness-Reference-Cited by-同舟云学术

New efficient Hadoop scheduler: Generalized particle swarm optimization and simulated annealing ‐ dominant resource fairness

Published:2022-11-25 Issue:4 Volume:35 Page:
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Sharma Sonia¹²^ORCID,Bharti Rajendra Kumar³

Affiliation:

1. Department of Computer Sciences Seth Jai Parkash Mukand Lal Institute of Engineering and Technology (JMIT) Yamunanagar India

2. Veer Madho Singh Bhandari Uttarakhand Technical University Dehradun India

3. CSE, Bipin Tripathi Kumaon Institute of Technology Dwarahat India

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.7528

Reference36 articles.

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2. GuoY WuL YuW WuB WangX.The improved job scheduling algorithm of Hadoop platform. arXiv preprint arXiv:1506.03004;2015.

3. HybSMRP: a hybrid scheduling algorithm in Hadoop MapReduce framework

4. HeC YingL SwansonD.Matchmaking: a new mapreduce scheduling technique. Proceedings of the 2011 IEEE Third International Conference on Cloud Computing Technology and Science;2011:40‐47; IEEE.

5. NguyenP SimonT HalemM ChapmanD LeQ.A hybrid scheduling algorithm for data intensive workloads in a mapreduce environment. Proceedings of the 2012 IEEE Fifth International Conference on Utility and Cloud Computing;2012:161‐167; IEEE.

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