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


Autoria(s): Xu, Xiaoyong; Tang, Maolin
Data(s)

01/06/2014

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/70354/

Publicador

IEEE

Relação

http://eprints.qut.edu.au/70354/1/cloud14-Xu%26Tang.pdf

DOI:10.1109/CLOUD.2014.44

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.

Direitos

Copyright 2014 Please consult the authors

Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #080108 Neural Evolutionary and Fuzzy Computation #080599 Distributed Computing not elsewhere classified #MapReduce #Cloud Computing #Big Data #Placement #Resource Management
Tipo

Conference Paper