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Understanding data center traffic characteristics

Open access
Benson, Theophilus, Anand, Ashok, Akella, Aditya, Zhang, Ming·2009·Proceedings of the 1st ACM workshop on Research on enterprise networking

Abstract

As data centers become more and more central in Internet communications, both research and operations communities have begun to explore how to better design and manage them. In this paper, we present a preliminary empirical study of end-to-end traffic patterns in data center networks that can inform and help evaluate research and operational approaches. We analyze SNMP logs collected at 19 data centers to examine temporal and spatial variations in link loads and losses. We find that while links in the core are heavily utilized the ones closer to the edge observe a greater degree of loss. We then study packet traces collected at a small number of switches in one data center and find evidence of ON-OFF traffic behavior. Finally, we develop a framework that derives ON-OFF traffic parameters for data center traffic sources that best explain the SNMP data collected for the data center. We show that the framework can be used to evaluate data center traffic engineering approaches. We are also applying the framework to design network-level traffic generators for data centers.

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Metadata

Authors
Benson, T., Anand, A., Akella, A., & Zhang, M.
Venue
Proceedings of the 1st ACM workshop on Research on enterprise networking
Publisher
ACM
Published
2009-08-21
Pages
65-72
OpenAlex
W2129424879
Cited by
447
References
0
FWCI
20.24
Type
conference paper
Language
en
Open access
gold

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CoChat. (2026). Citation check: Understanding data center traffic characteristics. https://app.cochat.ai/papers/10.1145/1592681.1592692
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Benson, T., Anand, A., Akella, A., & Zhang, M. (2009). Understanding data center traffic characteristics. Proceedings of the 1st ACM workshop on Research on enterprise networking. https://doi.org/10.1145/1592681.1592692

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.