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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 5
1321-1325

Resource efficiency optimization for big data mining algorithm with multi MapReduce collaboration scenario

Liao Bin1
Zhang Tao2
Yu Jiong2
Huang Jinglai1
Guo Binglei2
Liu Yan3
1. College of Statistics & Data Science, Xinjiang University of Finance & Economics, Urumqi 830012, China
2. School of Information Science & Engineering, Xinjiang University, Urumqi 830008, China
3. School of Software, Tsinghua University, Beijing 100084, China
DOI: 10.19734/j.issn.1001-3695.2018.11.0795

Abstract

Because any MapReduce job requires a series of complex operations such as task scheduling and resource allocation independently, there are a lot of redundant disk I/O and resource duplicate application operations among multiple MapReduce jobs coordinated by the same algorithm, causing inefficient resource utilization in job computing process. Big data mining algorithms are usually divided into several MapReduce Jobs, taking ItemBased algorithm as an example, this paper analyzed the resource efficiency of mining algorithm with multi-MapReduce job collaboration scenario. It proposed an ItemBased algorithm based on DistributedCache, which used DistributedCache to cache I/O data between multiple MapReduce Jobs, broke the defect of independence between jobs, and reduced the waiting delay between Map and Reduce tasks. The experimental results show that, DistributedCache can improve the data reading speed of MapReduce jobs. The algorithm reconstructed by Distribu-tedCache greatly reduces the waiting delay between Map and Reduce tasks, and improves the resource efficiency by more than three times.

Foundation Support

新疆维吾尔自治区自然科学基金资助项目(2016D01B014)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.11.0795
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 5
Section: Algorithm Research & Explore
Pages: 1321-1325
Serial Number: 1001-3695(2020)05-008-1321-05

Publish History

[2020-05-05] Printed Article

Cite This Article

廖彬, 张陶, 于炯, 等. 多MapReduce作业协同下的大数据挖掘类算法资源效率优化 [J]. 计算机应用研究, 2020, 37 (5): 1321-1325. (Liao Bin, Zhang Tao, Yu Jiong, et al. Resource efficiency optimization for big data mining algorithm with multi MapReduce collaboration scenario [J]. Application Research of Computers, 2020, 37 (5): 1321-1325. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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