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BigDataSDNSim: A simulator for analyzing big data applications in software-defined cloud data centers

Citation

Alwasel, K and Calheiros, RN and Garg, S and Buyya, R and Pathan, M and Georgakopoulos, D and Ranjan, R, BigDataSDNSim: A simulator for analyzing big data applications in software-defined cloud data centers, Software: Practice and Experience, 51, (5) pp. 893-920. ISSN 0038-0644 (2021) [Refereed Article]

Copyright Statement

2020 John Wiley & Sons, Ltd

DOI: doi:10.1002/spe.2917

Abstract

The integration and crosscoordination of big data processing and software-defined networking (SDN) are vital for improving the performance of big data applications. Various approaches for combining big data and SDN have been investigated by both industry and academia. However, empirical evaluations of solutions that combine big data processing and SDN are extremely costly and complicated. To address the problem of effective evaluation of solutions that combine big data processing with SDN, we present a new, self-contained simulation tool named BigDataSDNSim that enables the modeling and simulation of the big data management system YARN, its related programming models MapReduce, and SDN-enabled networks in a cloud computing environment. BigDataSDNSim supports cost-effective and easy to conduct experimentation in a controllable, repeatable, and configurable manner. The article illustrates the simulation accuracy and correctness of BigDataSDNSim by comparing the behavior and results of a real environment that combines big data processing and SDN with an equivalent simulated environment. Finally, the article presents two uses cases of BigDataSDNSim, which exhibit its practicality and features, illustrate the impact of data replication mechanisms of MapReduce in Hadoop YARN, and show the superiority of SDN over traditional networks to improve the performance of MapReduce applications.

Item Details

Item Type:Refereed Article
Keywords:big data, joint-optimization, MapReduce programming model, modeling and simulation, performance optimization, software-defined networking, cloud computing
Research Division:Information and Computing Sciences
Research Group:Distributed computing and systems software
Research Field:Cloud computing
Objective Division:Information and Communication Services
Objective Group:Communication technologies, systems and services
Objective Field:E-infrastructures
UTAS Author:Garg, S (Dr Saurabh Garg)
ID Code:146353
Year Published:2021
Web of Science® Times Cited:4
Deposited By:Information and Communication Technology
Deposited On:2021-09-01
Last Modified:2021-10-20
Downloads:0

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