An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach
Random Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Shahid Rajaee Teacher Training University
2014-07-01
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| Series: | Journal of Electrical and Computer Engineering Innovations |
| Subjects: | |
| Online Access: | https://jecei.sru.ac.ir/article_242_df31204b119ce993dcd5825327b524b6.pdf |
| _version_ | 1817993057271808000 |
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| author | S. Jamali N. Alipasandi B. Alipasandi |
| author_facet | S. Jamali N. Alipasandi B. Alipasandi |
| author_sort | S. Jamali |
| collection | DOAJ |
| description | Random Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed values is not an efficient solution. We propose a new method to dynamically tuning RED's parameters. For this purpose, we compute the rate of which the queue is occupied and consider it as a congestion metric that will be forecasted when the queue is overloaded. This meter is used to dynamically setting RED parameters. The simulation results show the effectiveness of the proposed method. According to the results, we achieve a significantly higher utilization and less packet loss comparing to original RED algorithm in dynamic conditions of the network. |
| first_indexed | 2024-04-14T01:34:42Z |
| format | Article |
| id | doaj.art-527a5b4ff9044d699501495309801962 |
| institution | Directory Open Access Journal |
| issn | 2322-3952 2345-3044 |
| language | English |
| last_indexed | 2024-04-14T01:34:42Z |
| publishDate | 2014-07-01 |
| publisher | Shahid Rajaee Teacher Training University |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering Innovations |
| spelling | doaj.art-527a5b4ff9044d6995014953098019622022-12-22T02:20:02ZengShahid Rajaee Teacher Training UniversityJournal of Electrical and Computer Engineering Innovations2322-39522345-30442014-07-0122576110.22061/jecei.2014.242242An Improvement over Random Early Detection Algorithm: A Self-Tuning ApproachS. Jamali0N. Alipasandi1B. Alipasandi2Computer Engineering Department, University of Mohaghegh Ardabili, Ardabil, IranSama technical and vocational training college, Islamic Azad University, Ardabil Branch, Ardabil, IranYoung Researchers and Elite Club, Ardabil Branch, Islamic Azad University, Ardabil, IranRandom Early Detection (RED) is one of the most commonly used Active Queue Management (AQM) algorithms that is recommended by IETF for deployment in the network. Although RED provides low average queuing delay and high throughput at the same time, but effectiveness of RED is highly sensitive to the RED parameters setting. As network condition varies largely, setting RED's parameters with fixed values is not an efficient solution. We propose a new method to dynamically tuning RED's parameters. For this purpose, we compute the rate of which the queue is occupied and consider it as a congestion metric that will be forecasted when the queue is overloaded. This meter is used to dynamically setting RED parameters. The simulation results show the effectiveness of the proposed method. According to the results, we achieve a significantly higher utilization and less packet loss comparing to original RED algorithm in dynamic conditions of the network.https://jecei.sru.ac.ir/article_242_df31204b119ce993dcd5825327b524b6.pdfinternetcongestion controlactive queue management (aqm)random early detection (red)self-tuning |
| spellingShingle | S. Jamali N. Alipasandi B. Alipasandi An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach Journal of Electrical and Computer Engineering Innovations internet congestion control active queue management (aqm) random early detection (red) self-tuning |
| title | An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach |
| title_full | An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach |
| title_fullStr | An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach |
| title_full_unstemmed | An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach |
| title_short | An Improvement over Random Early Detection Algorithm: A Self-Tuning Approach |
| title_sort | improvement over random early detection algorithm a self tuning approach |
| topic | internet congestion control active queue management (aqm) random early detection (red) self-tuning |
| url | https://jecei.sru.ac.ir/article_242_df31204b119ce993dcd5825327b524b6.pdf |
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