{"title":"统一的NDN数据平面名称查找数据结构","authors":"Miaomiao Liu, Tian Song, Yating Yang, Beichuan Zhang","doi":"10.1145/3125719.3132103","DOIUrl":null,"url":null,"abstract":"NDN data plane relays name-based packets by maintaining three tables: Content Store, Pending Interest Table and Forwarding Information Base. The three tables require similar but different schemes to be matched and updated in a nearly per-packet fashion, thus individual data structure is required for each table. In this work, we propose a unified data structure of name lookup for all three tables, namely CTrie, aiming at reducing the computational cost from three pipelined lookup rounds down to one unified round. CTrie extends the original Patricia trie to a combinational trie structure built from both component-based and byte-based hierarchical names. We compared CTrie with other approaches in speed and memory. The results show that CTrie runs 3.2 times faster and consumes about 38% memory than the current ones in terms of the whole data plane. CTrie fits for all application scenarios of NDN and especially well for IoT like lightweight-deployed scenarios.","PeriodicalId":394653,"journal":{"name":"Proceedings of the 4th ACM Conference on Information-Centric Networking","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"A unified data structure of name lookup for NDN data plane\",\"authors\":\"Miaomiao Liu, Tian Song, Yating Yang, Beichuan Zhang\",\"doi\":\"10.1145/3125719.3132103\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"NDN data plane relays name-based packets by maintaining three tables: Content Store, Pending Interest Table and Forwarding Information Base. The three tables require similar but different schemes to be matched and updated in a nearly per-packet fashion, thus individual data structure is required for each table. In this work, we propose a unified data structure of name lookup for all three tables, namely CTrie, aiming at reducing the computational cost from three pipelined lookup rounds down to one unified round. CTrie extends the original Patricia trie to a combinational trie structure built from both component-based and byte-based hierarchical names. We compared CTrie with other approaches in speed and memory. The results show that CTrie runs 3.2 times faster and consumes about 38% memory than the current ones in terms of the whole data plane. CTrie fits for all application scenarios of NDN and especially well for IoT like lightweight-deployed scenarios.\",\"PeriodicalId\":394653,\"journal\":{\"name\":\"Proceedings of the 4th ACM Conference on Information-Centric Networking\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 4th ACM Conference on Information-Centric Networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3125719.3132103\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 4th ACM Conference on Information-Centric Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3125719.3132103","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
摘要
NDN数据平面通过维护三个表(Content Store、Pending Interest Table和Forwarding Information Base)来转发基于名称的报文。这三个表需要以几乎每个数据包的方式匹配和更新相似但不同的模式,因此每个表都需要单独的数据结构。在这项工作中,我们提出了一个统一的名称查找数据结构,即CTrie,旨在将计算成本从三个流水线查找轮询减少到一个统一的轮询。CTrie将原来的Patricia trie扩展为基于组件和基于字节的层次结构名称构建的组合trie结构。我们将CTrie与其他方法在速度和内存方面进行了比较。结果表明,就整个数据平面而言,CTrie的运行速度比当前快3.2倍,消耗的内存约为38%。CTrie适用于NDN的所有应用场景,尤其适用于物联网(如轻量部署场景)。
A unified data structure of name lookup for NDN data plane
NDN data plane relays name-based packets by maintaining three tables: Content Store, Pending Interest Table and Forwarding Information Base. The three tables require similar but different schemes to be matched and updated in a nearly per-packet fashion, thus individual data structure is required for each table. In this work, we propose a unified data structure of name lookup for all three tables, namely CTrie, aiming at reducing the computational cost from three pipelined lookup rounds down to one unified round. CTrie extends the original Patricia trie to a combinational trie structure built from both component-based and byte-based hierarchical names. We compared CTrie with other approaches in speed and memory. The results show that CTrie runs 3.2 times faster and consumes about 38% memory than the current ones in terms of the whole data plane. CTrie fits for all application scenarios of NDN and especially well for IoT like lightweight-deployed scenarios.