EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite Network
Software-Defined Satellite Network (SDSN) plays an essential role in future networks. With the rapid development of networks and the constant enrichment of services, the number of filter rules in flow tables becomes enormous, which challenges the limited resources of on-board switches. For the memor...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9663201/ |
| _version_ | 1811309338762936320 |
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| author | Shuai Wang Kai Liu Chunxiao Jiang Jian Yan Linling Kuang |
| author_facet | Shuai Wang Kai Liu Chunxiao Jiang Jian Yan Linling Kuang |
| author_sort | Shuai Wang |
| collection | DOAJ |
| description | Software-Defined Satellite Network (SDSN) plays an essential role in future networks. With the rapid development of networks and the constant enrichment of services, the number of filter rules in flow tables becomes enormous, which challenges the limited resources of on-board switches. For the memory shortage of flow tables in SDSN, we propose an expanded-field search (EFS) algorithm, which supports storage compression during both the initialization and update of multistage flow-table. EFS considers the cost distinction between the static random-access memory (SRAM) and the ternary content addressable memory (TCAM). It applies a novel search strategy to expand the search field from <inline-formula> <tex-math notation="LaTeX">$O(N)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> for better convergence, and employs a statistical inference method to simplify the computation. Simulation results show that EFS takes a short run time and has a storage compression close to the global optimum, which outperforms existing algorithms significantly. In addition, the EFS also shows better performance in terms of search cost. |
| first_indexed | 2024-04-13T09:40:32Z |
| format | Article |
| id | doaj.art-f0f53a2f59f04582a5d3f0a2ea276c1a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T09:40:32Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f0f53a2f59f04582a5d3f0a2ea276c1a2022-12-22T02:51:56ZengIEEEIEEE Access2169-35362022-01-011039140010.1109/ACCESS.2021.31383999663201EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite NetworkShuai Wang0https://orcid.org/0000-0002-2326-3458Kai Liu1https://orcid.org/0000-0002-7331-4727Chunxiao Jiang2https://orcid.org/0000-0002-3703-121XJian Yan3https://orcid.org/0000-0003-4325-2948Linling Kuang4https://orcid.org/0000-0001-8283-855XSchool of Aerospace Engineering, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaBeijing National Research Center for Information Science and Technology, Tsinghua University, Beijing, ChinaSoftware-Defined Satellite Network (SDSN) plays an essential role in future networks. With the rapid development of networks and the constant enrichment of services, the number of filter rules in flow tables becomes enormous, which challenges the limited resources of on-board switches. For the memory shortage of flow tables in SDSN, we propose an expanded-field search (EFS) algorithm, which supports storage compression during both the initialization and update of multistage flow-table. EFS considers the cost distinction between the static random-access memory (SRAM) and the ternary content addressable memory (TCAM). It applies a novel search strategy to expand the search field from <inline-formula> <tex-math notation="LaTeX">$O(N)$ </tex-math></inline-formula> to <inline-formula> <tex-math notation="LaTeX">$O(N^{2})$ </tex-math></inline-formula> for better convergence, and employs a statistical inference method to simplify the computation. Simulation results show that EFS takes a short run time and has a storage compression close to the global optimum, which outperforms existing algorithms significantly. In addition, the EFS also shows better performance in terms of search cost.https://ieeexplore.ieee.org/document/9663201/Expanded-field searchmultistage flow-tablestorage optimizationsoftware-defined satellite network |
| spellingShingle | Shuai Wang Kai Liu Chunxiao Jiang Jian Yan Linling Kuang EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite Network IEEE Access Expanded-field search multistage flow-table storage optimization software-defined satellite network |
| title | EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite Network |
| title_full | EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite Network |
| title_fullStr | EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite Network |
| title_full_unstemmed | EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite Network |
| title_short | EFS: Efficient Storage Optimization for Multistage Flow-Table in Software-Defined Satellite Network |
| title_sort | efs efficient storage optimization for multistage flow table in software defined satellite network |
| topic | Expanded-field search multistage flow-table storage optimization software-defined satellite network |
| url | https://ieeexplore.ieee.org/document/9663201/ |
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