Efficient and Secure Traffic Scheduling Based on Private Sketch
In today’s data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods...
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MDPI AG
2025-01-01
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| author | Yang Chen Huishu Wu Xuhao Ren |
| author_facet | Yang Chen Huishu Wu Xuhao Ren |
| author_sort | Yang Chen |
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| description | In today’s data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods often rely on static rules or pre–defined policies, which make it difficult to cope with dynamically changing network traffic patterns. Additionally, the inability to efficiently manage tail contributors that disproportionately contribute to traffic can further exacerbate congestion issues. In this paper, we propose ESTS, an efficient and secure traffic scheduling based on private sketch, capable of identifying tail contributors to adjust routing and prevent congestion. The key idea is to develop a randomized admission (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>A</mi></mrow></semantics></math></inline-formula>) structure, linking two count–mean–min (CMM) sketches. The first CMM sketch records cold items, while the second, following the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>A</mi></mrow></semantics></math></inline-formula> structure, stores hot items with high frequency. Moreover, considering that tail contributors may leak private information, we incorporate Gaussian noise uniformly into the CMM sketch and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>A</mi></mrow></semantics></math></inline-formula> structure. Experimental evaluations on real and synthetic datasets demonstrate that ESTS significantly improves the accuracy of feature distribution estimation and privacy preservation. Compared to baseline methods, the ESTS framework achieves a 25% reduction in average relative error and a 30% improvement in tail contributor identification accuracy. These results underline the framework’s efficiency and reliability. |
| format | Article |
| id | doaj-art-67a60a911d0b41649aa6d255d08b5d7b |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-67a60a911d0b41649aa6d255d08b5d7b2025-01-24T13:40:03ZengMDPI AGMathematics2227-73902025-01-0113228810.3390/math13020288Efficient and Secure Traffic Scheduling Based on Private SketchYang Chen0Huishu Wu1Xuhao Ren2Beijing Institute of Tracking and Telecommunication Technology, Beijing 100094, ChinaFaculty of Law, University of Montreal, 2900 Edouard Montpetit Blvd, Montreal, QC H3T 1J4, CanadaSchool of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing 100081, ChinaIn today’s data–driven world, the explosive growth of network traffic often leads to network congestion, which seriously affects service performance and user experience. Network traffic scheduling is one of the key technologies to deal with congestion problems. Traditional traffic scheduling methods often rely on static rules or pre–defined policies, which make it difficult to cope with dynamically changing network traffic patterns. Additionally, the inability to efficiently manage tail contributors that disproportionately contribute to traffic can further exacerbate congestion issues. In this paper, we propose ESTS, an efficient and secure traffic scheduling based on private sketch, capable of identifying tail contributors to adjust routing and prevent congestion. The key idea is to develop a randomized admission (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>A</mi></mrow></semantics></math></inline-formula>) structure, linking two count–mean–min (CMM) sketches. The first CMM sketch records cold items, while the second, following the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>A</mi></mrow></semantics></math></inline-formula> structure, stores hot items with high frequency. Moreover, considering that tail contributors may leak private information, we incorporate Gaussian noise uniformly into the CMM sketch and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>R</mi><mi>A</mi></mrow></semantics></math></inline-formula> structure. Experimental evaluations on real and synthetic datasets demonstrate that ESTS significantly improves the accuracy of feature distribution estimation and privacy preservation. Compared to baseline methods, the ESTS framework achieves a 25% reduction in average relative error and a 30% improvement in tail contributor identification accuracy. These results underline the framework’s efficiency and reliability.https://www.mdpi.com/2227-7390/13/2/288private sketchtraffic schedulingtail contributors |
| spellingShingle | Yang Chen Huishu Wu Xuhao Ren Efficient and Secure Traffic Scheduling Based on Private Sketch Mathematics private sketch traffic scheduling tail contributors |
| title | Efficient and Secure Traffic Scheduling Based on Private Sketch |
| title_full | Efficient and Secure Traffic Scheduling Based on Private Sketch |
| title_fullStr | Efficient and Secure Traffic Scheduling Based on Private Sketch |
| title_full_unstemmed | Efficient and Secure Traffic Scheduling Based on Private Sketch |
| title_short | Efficient and Secure Traffic Scheduling Based on Private Sketch |
| title_sort | efficient and secure traffic scheduling based on private sketch |
| topic | private sketch traffic scheduling tail contributors |
| url | https://www.mdpi.com/2227-7390/13/2/288 |
| work_keys_str_mv | AT yangchen efficientandsecuretrafficschedulingbasedonprivatesketch AT huishuwu efficientandsecuretrafficschedulingbasedonprivatesketch AT xuhaoren efficientandsecuretrafficschedulingbasedonprivatesketch |