A Trusted NUMFabric Algorithm for Congestion Price Calculation at the Internet-of-Things Datacenter Article Swipe
YOU?
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· 2021
· Open Access
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· DOI: https://doi.org/10.32604/cmes.2021.012230
The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information. However, it is difficult to predict the congestion state of the link-end accurately at the source. In this paper, we presented an improved NUMFabric algorithm for calculating the overall congestion price. In the proposed scheme, the whole network structure had been obtained by the central control server in the Software Defined Network, and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated. In this scheme, the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information. Based on the historical data, the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm. The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm. We evaluate this evolved NUMFabric algorithm in NS3, which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmes.2021.012230
- OA Status
- diamond
- Cited By
- 3
- References
- 29
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3135486929
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3135486929Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.32604/cmes.2021.012230Digital Object Identifier
- Title
-
A Trusted NUMFabric Algorithm for Congestion Price Calculation at the Internet-of-Things DatacenterWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Chun Shan, Xiaolong Chen, Guoqiang Deng, Hao LiuList of authors in order
- Landing page
-
https://doi.org/10.32604/cmes.2021.012230Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://doi.org/10.32604/cmes.2021.012230Direct OA link when available
- Concepts
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Computer science, Network congestion, Cloud computing, OpenFlow, Computer network, Algorithm, The Internet, Explicit Congestion Notification, Software-defined networking, State (computer science), Distributed computing, TCP tuning, Network packet, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 1, 2022: 2Per-year citation counts (last 5 years)
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29Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.structure | 71 |
| abstract_inverted_index.accurately | 45, 151 |
| abstract_inverted_index.algorithm. | 157, 183 |
| abstract_inverted_index.calculated | 173 |
| abstract_inverted_index.congestion | 6, 29, 40, 62, 95, 140 |
| abstract_inverted_index.historical | 137 |
| abstract_inverted_index.calculating | 59, 92 |
| abstract_inverted_index.efficiently | 200 |
| abstract_inverted_index.intelligent | 127 |
| abstract_inverted_index.utilization | 205 |
| abstract_inverted_index.Opendaylight | 116 |
| abstract_inverted_index.datacenters. | 210 |
| abstract_inverted_index.demonstrated | 193 |
| abstract_inverted_index.information. | 32, 133 |
| abstract_inverted_index.demonstrated. | 99 |
| abstract_inverted_index.dual-hierarchy | 89 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.70299111 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |