A more efficient and effective heuristic algorithm for the MapReduce placement problem in cloud computing

Xu, Xiaoyong & Tang, Maolin (2014) A more efficient and effective heuristic algorithm for the MapReduce placement problem in cloud computing. In Proceedings of the 2014 International Conference on Cloud Computing, IEEE, Anchorage, Alaska, USA, pp. 264-271.

View at publisher

Abstract

The placement of the mappers and reducers on the machines directly affects the performance and cost of the MapReduce computation in cloud computing. From the computational point of view, the mappers/reducers placement problem is a generalization of the classical bin packing problem, which is NP-complete. Thus, in this paper we propose a new heuristic algorithm for the mappers/reducers placement problem in cloud computing and evaluate it by comparing with other several heuristics on solution quality and computation time by solving a set of test problems with various characteristics. The computational results show that our heuristic algorithm is much more efficient than the other heuristics. Also, we verify the effectiveness of our heuristic algorithm by comparing the mapper/reducer placement for a benchmark problem generated by our heuristic algorithm with a conventional mapper/reducer placement. The comparison results show that the computation using our mapper/reducer placement is much cheaper while still satisfying the computation deadline.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

Full-text downloads:

82 since deposited on 22 Apr 2014
15 in the past twelve months

Full-text downloads displays the total number of times this work’s files (e.g., a PDF) have been downloaded from QUT ePrints as well as the number of downloads in the previous 365 days. The count includes downloads for all files if a work has more than one.

ID Code: 70354
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: MapReduce, Cloud Computing, Big Data, Placement, Resource Management
DOI: 10.1109/CLOUD.2014.44
ISBN: 978-1-4799-5062-1
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Neural Evolutionary and Fuzzy Computation (080108)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > DISTRIBUTED COMPUTING (080500) > Distributed Computing not elsewhere classified (080599)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Please consult the authors
Deposited On: 22 Apr 2014 05:09
Last Modified: 02 Feb 2015 05:30

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page