Distributed control framework for mapreduce cloud on cloud computing

Tzu Chi Huang, Kuo Chih Chu, Guo Hao Huang, Yan Chen Shen, Ce Kuen Shieh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

A MapReduce cloud becomes a key to the success of cloud computing today. However, a MapReduce cloud uses a single Master node as the brain to manage tasks distributed over Slave nodes for controlling the entire progress of the application execution. Accordingly, a MapReduce cloud easily overloads the Master node with reports sent from Slave nodes at run time to harm performance. Besides, a MapReduce cloud makes the Master node a single failure point to suspend the application execution when the Master node cannot work. A MapReduce cloud can use the Distributed Control Framework (DCF) proposed in this paper to improve both performance and fault tolerance, because DCF shifts most works of a Master node to a DCF Master Agent coexisting in each Slave node and allows Slave nodes to join or leave a cloud at run time without interrupting the application execution. According to observations on experiments with various applications in this paper, a MapReduce cloud can use DCF to have better performance and fault tolerance in comparison to a native MapReduce cloud.

Original languageEnglish
Title of host publicationIEEE/IFIP Network Operations and Management Symposium
Subtitle of host publicationCognitive Management in a Cyber World, NOMS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-4
Number of pages4
ISBN (Electronic)9781538634165
DOIs
Publication statusPublished - 2018 Jul 6
Event2018 IEEE/IFIP Network Operations and Management Symposium, NOMS 2018 - Taipei, Taiwan
Duration: 2018 Apr 232018 Apr 27

Publication series

NameIEEE/IFIP Network Operations and Management Symposium: Cognitive Management in a Cyber World, NOMS 2018

Other

Other2018 IEEE/IFIP Network Operations and Management Symposium, NOMS 2018
Country/TerritoryTaiwan
CityTaipei
Period18-04-2318-04-27

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Information Systems and Management
  • Control and Optimization

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