Hadoop MapReduce Scheduling Paradigms

被引：0

作者：

Johannessen, Roger ^{[1
]}

Yazidi, Anis ^{[1
]}

Feng, Boning ^{[1
]}

机构：

[1] Oslo & Akershus Univ, Coll Appl Sci, Dept Comp Sci, Oslo, Norway

来源：

2017 2ND IEEE INTERNATIONAL CONFERENCE ON CLOUD COMPUTING AND BIG DATA ANALYSIS (ICCCBDA 2017) | 2017年

关键词：

Apache Hadoop; MapReduce; scheduling paradigms;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Apache Hadoop is one of the most prominent and early technologies for handling big data. Different scheduling algorithms within the framework of Apache Hadoop were developed in the last decade. In this paper, we attempt to provide a comprehensive overview over the different paradigms for scheduling in Apache Hadoop. The surveyed approaches fall under different categories, namely, Deadline prioritization, Resource prioritization, Job size prioritization, Hybrid approaches and recent trends for improvements upon default schedulers.

引用

页码：175 / 179

页数：5

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