ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations
To solve the problem of resource shortage of ternary content addressable memory (TCAM) in the data plane of software defined network (SDN), a deep flow table aggregation method was proposed based on content entry trees, and a storage architecture of large-scale SDN flow tables named ADAFT was establ...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Department of Journal on Communications
2024-05-01
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| Series: | Tongxin xuebao |
| Subjects: | |
| Online Access: | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024059/ |
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| _version_ | 1841539224479727616 |
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| author | XIONG Bing YUAN Yue ZHAO Jinyuan ZHAO Baokang HE Shiming ZHANG Jin |
| author_facet | XIONG Bing YUAN Yue ZHAO Jinyuan ZHAO Baokang HE Shiming ZHANG Jin |
| author_sort | XIONG Bing |
| collection | DOAJ |
| description | To solve the problem of resource shortage of ternary content addressable memory (TCAM) in the data plane of software defined network (SDN), a deep flow table aggregation method was proposed based on content entry trees, and a storage architecture of large-scale SDN flow tables named ADAFT was established. The architecture relaxed the Hamming distance requirement between ag-gregated flow entries, and a content entry tree was constructed to aggregate flow entries with different action sets, for significantly en-hancing the aggregation degree of flow tables. Then a dynamic limitation mechanism was designed for the height of content entry trees based on the awareness of TCAM load ratio, to minimize the lookup overhead of aggregated flow tables. Meanwhile, an adaptive selec-tion strategy of flow entry aggregation was presented in the light of TCAM load ratio, to strike a balance between the aggregation degree and lookup overhead of flow tables. Experimental results indicate that the ADAFT architecture achieves much higher flow table com-pression ratios up to 65.74% than existing methods. |
| format | Article |
| id | doaj-art-b33327a647fb4539886790aa09d9353d |
| institution | Kabale University |
| issn | 1000-436X |
| language | zho |
| publishDate | 2024-05-01 |
| publisher | Editorial Department of Journal on Communications |
| record_format | Article |
| series | Tongxin xuebao |
| spelling | doaj-art-b33327a647fb4539886790aa09d9353d2025-01-14T07:24:19ZzhoEditorial Department of Journal on CommunicationsTongxin xuebao1000-436X2024-05-014522623862276487ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregationsXIONG BingYUAN YueZHAO JinyuanZHAO BaokangHE ShimingZHANG JinTo solve the problem of resource shortage of ternary content addressable memory (TCAM) in the data plane of software defined network (SDN), a deep flow table aggregation method was proposed based on content entry trees, and a storage architecture of large-scale SDN flow tables named ADAFT was established. The architecture relaxed the Hamming distance requirement between ag-gregated flow entries, and a content entry tree was constructed to aggregate flow entries with different action sets, for significantly en-hancing the aggregation degree of flow tables. Then a dynamic limitation mechanism was designed for the height of content entry trees based on the awareness of TCAM load ratio, to minimize the lookup overhead of aggregated flow tables. Meanwhile, an adaptive selec-tion strategy of flow entry aggregation was presented in the light of TCAM load ratio, to strike a balance between the aggregation degree and lookup overhead of flow tables. Experimental results indicate that the ADAFT architecture achieves much higher flow table com-pression ratios up to 65.74% than existing methods.http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024059/software defined networklarge-scale SDN flow tablecontent entry treeadaptive deep aggregationTCAM load ratio awareness |
| spellingShingle | XIONG Bing YUAN Yue ZHAO Jinyuan ZHAO Baokang HE Shiming ZHANG Jin ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations Tongxin xuebao software defined network large-scale SDN flow table content entry tree adaptive deep aggregation TCAM load ratio awareness |
| title | ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations |
| title_full | ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations |
| title_fullStr | ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations |
| title_full_unstemmed | ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations |
| title_short | ADAFT：an storage architecture of large-scale SDN flow tables based on adaptive deep aggregations |
| title_sort | adaft an storage architecture of large scale sdn flow tables based on adaptive deep aggregations |
| topic | software defined network large-scale SDN flow table content entry tree adaptive deep aggregation TCAM load ratio awareness |
| url | http://www.joconline.com.cn/zh/article/doi/10.11959/j.issn.1000-436x.2024059/ |
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