TY - JOUR
T1 - BigDataSDNSim : a simulator for analyzing big data applications in software-defined cloud data centers
AU - Alwasel, Khaled
AU - Calheiros, Rodrigo N.
AU - Garg, Saurabh
AU - Buyya, Rajkumar
AU - Pathan, Mukaddim
AU - Georgakopoulos, Dimitrios
AU - Ranjan, Rajiv
N1 - Publisher Copyright:
© 2020 John Wiley & Sons Ltd
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
UR - https://hdl.handle.net/1959.7/uws:61367
U2 - 10.1002/spe.2917
DO - 10.1002/spe.2917
M3 - Article
SN - 0038-0644
VL - 51
SP - 893
EP - 920
JO - Software: Practice and Experience
JF - Software: Practice and Experience
IS - 5
ER -