Modeling and Generating Control-Plane Traffic for Cellular Networks Article Swipe
Jiayi Meng
,
Jingqi Huang
,
Yuanming Hu
,
Yaron Koral
,
Xiaojun Lin
,
Muhammad Shahbaz
,
A. Sharma
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3618257.3624808
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1145/3618257.3624808
With 5G deployment gaining momentum, the control-plane traffic volume of cellular networks is escalating. Such rapid traffic growth motivates the need to study the mobile core network (MCN) control-plane design and performance optimization. Doing so requires realistic, large control-plane traffic traces in order to profile and debug the mobile network performance under real workload. However, large-scale control-plane traffic traces are not made available to the public by mobile operators due to business and privacy concerns. As such, it is critically important to develop accurate, scalable, versatile, and open-to-innovation control traffic generators, which in turn critically rely on an accurate traffic model for the control plane. Developing an accurate model of control-plane traffic faces several challenges: (1) how to capture the dependence among the control events generated by each User Equipment (UE), (2) how to model the inter-arrival time and sojourn time of control events of individual UEs, and (3) how to capture the diversity of control-plane traffic across UEs. We present a novel two-level hierarchical state-machine-based control-plane traffic model. We further show how our model can be easily adjusted from LTE to NextG networks (e.g., 5G) to support modeling future control-plane traffic. We experimentally validate that the proposed model can generate large realistic control-plane traffic traces. We have open-sourced our traffic generator to the public to foster MCN research.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3618257.3624808
- https://dl.acm.org/doi/pdf/10.1145/3618257.3624808
- OA Status
- gold
- Cited By
- 5
- References
- 46
- Related Works
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- OpenAlex ID
- https://openalex.org/W4387881008
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4387881008Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3618257.3624808Digital Object Identifier
- Title
-
Modeling and Generating Control-Plane Traffic for Cellular NetworksWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-10-23Full publication date if available
- Authors
-
Jiayi Meng, Jingqi Huang, Yuanming Hu, Yaron Koral, Xiaojun Lin, Muhammad Shahbaz, A. SharmaList of authors in order
- Landing page
-
https://doi.org/10.1145/3618257.3624808Publisher landing page
- PDF URL
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https://dl.acm.org/doi/pdf/10.1145/3618257.3624808Direct link to full text PDF
- Open access
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YesWhether a free full text is available
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-
goldOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3618257.3624808Direct OA link when available
- Concepts
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Computer science, Traffic generation model, Scalability, Cellular network, Control (management), Computer network, Artificial intelligence, DatabaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 4Per-year citation counts (last 5 years)
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46Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.real | 52 |
| abstract_inverted_index.rely | 95 |
| abstract_inverted_index.show | 171 |
| abstract_inverted_index.that | 195 |
| abstract_inverted_index.time | 137, 140 |
| abstract_inverted_index.turn | 93 |
| abstract_inverted_index.(MCN) | 27 |
| abstract_inverted_index.(UE), | 130 |
| abstract_inverted_index.Doing | 33 |
| abstract_inverted_index.NextG | 182 |
| abstract_inverted_index.among | 121 |
| abstract_inverted_index.debug | 46 |
| abstract_inverted_index.faces | 112 |
| abstract_inverted_index.large | 37, 201 |
| abstract_inverted_index.model | 100, 108, 134, 174, 198 |
| abstract_inverted_index.novel | 162 |
| abstract_inverted_index.order | 42 |
| abstract_inverted_index.rapid | 15 |
| abstract_inverted_index.study | 22 |
| abstract_inverted_index.such, | 76 |
| abstract_inverted_index.under | 51 |
| abstract_inverted_index.which | 91 |
| abstract_inverted_index.(e.g., | 184 |
| abstract_inverted_index.across | 157 |
| abstract_inverted_index.design | 29 |
| abstract_inverted_index.easily | 177 |
| abstract_inverted_index.events | 124, 143 |
| abstract_inverted_index.foster | 216 |
| abstract_inverted_index.future | 189 |
| abstract_inverted_index.growth | 17 |
| abstract_inverted_index.mobile | 24, 48, 67 |
| abstract_inverted_index.model. | 168 |
| abstract_inverted_index.plane. | 104 |
| abstract_inverted_index.public | 65, 214 |
| abstract_inverted_index.traces | 40, 58 |
| abstract_inverted_index.volume | 8 |
| abstract_inverted_index.capture | 118, 151 |
| abstract_inverted_index.control | 88, 103, 123, 142 |
| abstract_inverted_index.develop | 82 |
| abstract_inverted_index.further | 170 |
| abstract_inverted_index.gaining | 3 |
| abstract_inverted_index.network | 26, 49 |
| abstract_inverted_index.present | 160 |
| abstract_inverted_index.privacy | 73 |
| abstract_inverted_index.profile | 44 |
| abstract_inverted_index.several | 113 |
| abstract_inverted_index.sojourn | 139 |
| abstract_inverted_index.support | 187 |
| abstract_inverted_index.traces. | 205 |
| abstract_inverted_index.traffic | 7, 16, 39, 57, 89, 99, 111, 156, 167, 204, 210 |
| abstract_inverted_index.However, | 54 |
| abstract_inverted_index.accurate | 98, 107 |
| abstract_inverted_index.adjusted | 178 |
| abstract_inverted_index.business | 71 |
| abstract_inverted_index.cellular | 10 |
| abstract_inverted_index.generate | 200 |
| abstract_inverted_index.modeling | 188 |
| abstract_inverted_index.networks | 11, 183 |
| abstract_inverted_index.proposed | 197 |
| abstract_inverted_index.requires | 35 |
| abstract_inverted_index.traffic. | 191 |
| abstract_inverted_index.validate | 194 |
| abstract_inverted_index.Equipment | 129 |
| abstract_inverted_index.accurate, | 83 |
| abstract_inverted_index.available | 62 |
| abstract_inverted_index.concerns. | 74 |
| abstract_inverted_index.diversity | 153 |
| abstract_inverted_index.generated | 125 |
| abstract_inverted_index.generator | 211 |
| abstract_inverted_index.important | 80 |
| abstract_inverted_index.momentum, | 4 |
| abstract_inverted_index.motivates | 18 |
| abstract_inverted_index.operators | 68 |
| abstract_inverted_index.realistic | 202 |
| abstract_inverted_index.research. | 218 |
| abstract_inverted_index.scalable, | 84 |
| abstract_inverted_index.two-level | 163 |
| abstract_inverted_index.workload. | 53 |
| abstract_inverted_index.Developing | 105 |
| abstract_inverted_index.critically | 79, 94 |
| abstract_inverted_index.dependence | 120 |
| abstract_inverted_index.deployment | 2 |
| abstract_inverted_index.individual | 145 |
| abstract_inverted_index.realistic, | 36 |
| abstract_inverted_index.versatile, | 85 |
| abstract_inverted_index.challenges: | 114 |
| abstract_inverted_index.escalating. | 13 |
| abstract_inverted_index.generators, | 90 |
| abstract_inverted_index.large-scale | 55 |
| abstract_inverted_index.performance | 31, 50 |
| abstract_inverted_index.hierarchical | 164 |
| abstract_inverted_index.open-sourced | 208 |
| abstract_inverted_index.control-plane | 6, 28, 38, 56, 110, 155, 166, 190, 203 |
| abstract_inverted_index.inter-arrival | 136 |
| abstract_inverted_index.optimization. | 32 |
| abstract_inverted_index.experimentally | 193 |
| abstract_inverted_index.open-to-innovation | 87 |
| abstract_inverted_index.state-machine-based | 165 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.6499999761581421 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.80292034 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |