An adaptive approach for elephant flow detection with the rapidly changing traffic in data center network Article Swipe
Zehui Liu
,
Deyun Gao
,
Ying Liu
,
Hongke Zhang
,
Chuan Heng Foh
·
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1002/nem.1987
YOU?
·
· 2017
· Open Access
·
· DOI: https://doi.org/10.1002/nem.1987
Summary Software‐defined network, which separates control plane from the underlying physical devices, has the advantages of global visibility and high flexibility. Among the most typical applications in software‐defined network, there is significant interest on classifying flows, especially for elephant flow detection. Previous studies show that detecting and rerouting elephant flows (flows that transfer significant amount of data) effectively can lead to a 113% improvement in aggregate throughput compared with the traditional routing. However, the threshold of the existing detection approach was preconfigured without the consideration of the rapidly changing traffic in data center networks. This phenomenon could cause high detection error rate. To address this problem, we propose an adaptive approach for elephant flow detection, which could efficiently identify elephant flows with low latency and low overhead. Particularly, to meet the demands of the traffic characteristics in data center networks, dynamical traffic learning algorithm is adopted to configure the threshold value real timely and dynamically. Numerical results and experimental tests show that the mean error rate of detection is only 4.61% and the maximum number of packet‐in messages is minimum compared to other methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/nem.1987
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/nem.1987
- OA Status
- hybrid
- Cited By
- 14
- References
- 22
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2738753737
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2738753737Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/nem.1987Digital Object Identifier
- Title
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An adaptive approach for elephant flow detection with the rapidly changing traffic in data center networkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2017Year of publication
- Publication date
-
2017-07-24Full publication date if available
- Authors
-
Zehui Liu, Deyun Gao, Ying Liu, Hongke Zhang, Chuan Heng FohList of authors in order
- Landing page
-
https://doi.org/10.1002/nem.1987Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/nem.1987Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/nem.1987Direct OA link when available
- Concepts
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Computer science, Data center, Network packet, Real-time computing, Software-defined networking, Overhead (engineering), Latency (audio), Visibility, Computer network, Distributed computing, Operating system, Telecommunications, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 1, 2023: 2, 2022: 2, 2021: 1, 2020: 4Per-year citation counts (last 5 years)
- References (count)
-
22Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| publication_year | 2017 |
| referenced_works | https://openalex.org/W2110722699, https://openalex.org/W2147118406, https://openalex.org/W2168595508, https://openalex.org/W2062832101, https://openalex.org/W4237515752, https://openalex.org/W1975379406, https://openalex.org/W2072298849, https://openalex.org/W4253729210, https://openalex.org/W2007560170, https://openalex.org/W2164998096, https://openalex.org/W2040340473, https://openalex.org/W2150093579, https://openalex.org/W4236202168, https://openalex.org/W2099562536, https://openalex.org/W2493772525, https://openalex.org/W2024921146, https://openalex.org/W2280926163, https://openalex.org/W3006383271, https://openalex.org/W2097364154, https://openalex.org/W2161336914, https://openalex.org/W2022758041, https://openalex.org/W2130531694 |
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| corresponding_author_ids | https://openalex.org/A5100414083 |
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| corresponding_institution_ids | https://openalex.org/I21193070 |
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| citation_normalized_percentile.is_in_top_10_percent | False |