On the optimal number of computational resources in MapReduce

Htway Htway Hlaing*, Hidehiro Kanemitsu, Tatsuo Nakajima, Hidenori Nakazato

*この研究の対応する著者

研究成果: Conference contribution

抄録

Big data computing in the cloud needs faster processing and better resource provisioning. MapReduce is the framework for computing large scale datasets in cloud environments. Optimization of resource requirement for each job to satisfy a specific objective in MapReduce is an open problem. Many factors, e.g., system side information and requirements of each client must be considered to estimate the appropriate amount of resources. This paper presents a mathematical model for the optimal number of map tasks in MapReduce resource provisioning. This model is to estimate the optimal number of the mappers based on the resource specification and the size of the dataset.

本文言語English
ホスト出版物のタイトルCloud Computing – CLOUD 2019 - 12th International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings
編集者Qingyang Wang, Liang-Jie Zhang, Dilma Da Silva
出版社Springer-Verlag
ページ240-252
ページ数13
ISBN(印刷版)9783030235017
DOI
出版ステータスPublished - 2019 1月 1
イベント12th International Conference on Cloud Computing, CLOUD 2019 held as part of the Services Conference Federation, SCF 2019 - San Diego, United States
継続期間: 2019 6月 252019 6月 30

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11513 LNCS
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Conference

Conference12th International Conference on Cloud Computing, CLOUD 2019 held as part of the Services Conference Federation, SCF 2019
国/地域United States
CitySan Diego
Period19/6/2519/6/30

ASJC Scopus subject areas

  • 理論的コンピュータサイエンス
  • コンピュータ サイエンス(全般)

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