A Sketch-Based Fine-Grained Proportional Integral Queue Management Method
The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQ...
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| Format: | Article |
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MDPI AG
2023-08-01
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| Series: | Axioms |
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| Online Access: | https://www.mdpi.com/2075-1680/12/9/814 |
| _version_ | 1797581305406291968 |
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| author | Haiting Zhu Hu Sun Yixin Jiang Gaofeng He Lu Zhang Yin Lu |
| author_facet | Haiting Zhu Hu Sun Yixin Jiang Gaofeng He Lu Zhang Yin Lu |
| author_sort | Haiting Zhu |
| collection | DOAJ |
| description | The phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service. |
| first_indexed | 2024-03-10T23:03:32Z |
| format | Article |
| id | doaj.art-624b842854904c8fb0fe25ec80a72875 |
| institution | Directory Open Access Journal |
| issn | 2075-1680 |
| language | English |
| last_indexed | 2024-03-10T23:03:32Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Axioms |
| spelling | doaj.art-624b842854904c8fb0fe25ec80a728752023-11-19T09:32:06ZengMDPI AGAxioms2075-16802023-08-0112981410.3390/axioms12090814A Sketch-Based Fine-Grained Proportional Integral Queue Management MethodHaiting Zhu0Hu Sun1Yixin Jiang2Gaofeng He3Lu Zhang4Yin Lu5School of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaSchool of Computer Science, Nanjing Audit University, Nanjing 211815, ChinaSchool of Internet of Things, Nanjing University of Posts and Telecommunications, Nanjing 210003, ChinaThe phenomenon “bufferbloat” occurs when the buffers of the network intermediary nodes fill up, causing long queuing delays. This has a significant negative impact on the quality of service of network applications, particularly those that are sensitive to time delay. Many active queue management (AQM) algorithms have been proposed to overcome this problem. Those AQMs attempt to maintain minimal queuing delays and good throughput by purposefully dropping packets at network intermediary nodes. However, the existing AQM algorithms mostly drop packets randomly based on a certain metric such as queue length or queuing delay, which fails to achieve fine-grained differentiation of data streams. In this paper, we propose a fine-grained sketch-based proportional integral queue management algorithm S-PIE, which uses an additional measurement structure Sketch for packet frequency share judgment based on the existing PIE algorithm for the fine-grained differentiation between data streams and adjust the drop policy for a differentiated packet drop. Experimental results on the NS-3 simulation platform show that the S-PIE algorithm achieves lower average queue length and RTT and higher fairness than PIE, RED, and CoDel algorithms while maintaining a similar throughput performance, maintaining network availability and stability, and improving network quality of service.https://www.mdpi.com/2075-1680/12/9/814queue managementfrequency countingdifferentiated packet dropfine-grained identificationfairness |
| spellingShingle | Haiting Zhu Hu Sun Yixin Jiang Gaofeng He Lu Zhang Yin Lu A Sketch-Based Fine-Grained Proportional Integral Queue Management Method Axioms queue management frequency counting differentiated packet drop fine-grained identification fairness |
| title | A Sketch-Based Fine-Grained Proportional Integral Queue Management Method |
| title_full | A Sketch-Based Fine-Grained Proportional Integral Queue Management Method |
| title_fullStr | A Sketch-Based Fine-Grained Proportional Integral Queue Management Method |
| title_full_unstemmed | A Sketch-Based Fine-Grained Proportional Integral Queue Management Method |
| title_short | A Sketch-Based Fine-Grained Proportional Integral Queue Management Method |
| title_sort | sketch based fine grained proportional integral queue management method |
| topic | queue management frequency counting differentiated packet drop fine-grained identification fairness |
| url | https://www.mdpi.com/2075-1680/12/9/814 |
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