Paper Info
Title
Flow-level traffic matrix generation for various data center networks
Abstract
The number of data centers deployed by governments, enterprises, and universities has been increased affected by the development of cloud computing technologies to reduce CAPAX and OPEX. Many architectures or topologies for data center networks have been proposed to address the diverse purposes and requirements. However, the construction of data centers incurs significant costs. Moreover, there are many technologies that can affect the structure of the data center. Before building a data center, it must be confirmed that it possesses the characteristics necessary to satisfy requirements. Efficient ways to find and confirm network characteristics include simulation and tests using a traffic generation method. Our proposed method is designed to generate network traffic that address many characteristics of data center networks explored by several studies. The proposed method generates network traffic utilizing flow-level traffic matrix, not directly generates packets. We used Python programming language to create traffic matrix and iPerf to generate network packets. To evaluate it, we compared the generation results to real network traffic collected from a data center network. The result shows that the generated traffic is similar with the real network traffic.
Year
DOI
Venue
2014
10.1109/NOMS.2014.6838394
NOMS
Keywords
Field
DocType
opex,flow-level traffic matrix generation,network traffic,computer centres,traffic generation method,data center networks,network packets,python programming language,capax,traffic generator,telecommunication traffic,flow-level traffic,generators,network topology,protocols,servers,shape,topology
Traffic generation model,Computer science,Floating car data,Network packet,Computer network,Network topology,Traffic shaping,Data center,Network traffic simulation,Network traffic control,Distributed computing
Conference
ISSN
Citations 
PageRank 
1542-1201
1
0.36
References 
Authors
11
5
Name
Order
Citations
PageRank
Yoonseon Han1296.03
Sin-Seok Seo2879.84
Chan Kyou Hwang323.41
Yoo Jae Hyoung47419.35
James Won-Ki Hong5713122.26