{"title":"如何处理基于范围的包分类器","authors":"Vitalii Demianiuk, Kirill Kogan","doi":"10.1145/3314148.3314346","DOIUrl":null,"url":null,"abstract":"Efficient representations of multi-field packet classifiers with fields represented by ranges is a core mechanism to express services on data plane. To implement classifiers in ternary-addressable memory (TCAM), each range should be encoded into multiple ternary bit strings whose number is at most linear to the width (in bits) of a represented field independently from range encoding method. In this paper we introduce a notion of a subrange allowing to represent a field range on any chosen subset of bit indices that significantly improve efficiency of classifier representations. Our analytic results are confirmed with a comprehensive evaluation study showing applicability of our approach to implement desired levels of expressiveness and scalability in packet classifiers.","PeriodicalId":346870,"journal":{"name":"Proceedings of the 2019 ACM Symposium on SDN Research","volume":"84 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"How to deal with range-based packet classifiers\",\"authors\":\"Vitalii Demianiuk, Kirill Kogan\",\"doi\":\"10.1145/3314148.3314346\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Efficient representations of multi-field packet classifiers with fields represented by ranges is a core mechanism to express services on data plane. To implement classifiers in ternary-addressable memory (TCAM), each range should be encoded into multiple ternary bit strings whose number is at most linear to the width (in bits) of a represented field independently from range encoding method. In this paper we introduce a notion of a subrange allowing to represent a field range on any chosen subset of bit indices that significantly improve efficiency of classifier representations. Our analytic results are confirmed with a comprehensive evaluation study showing applicability of our approach to implement desired levels of expressiveness and scalability in packet classifiers.\",\"PeriodicalId\":346870,\"journal\":{\"name\":\"Proceedings of the 2019 ACM Symposium on SDN Research\",\"volume\":\"84 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 ACM Symposium on SDN Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3314148.3314346\",\"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 2019 ACM Symposium on SDN Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3314148.3314346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Efficient representations of multi-field packet classifiers with fields represented by ranges is a core mechanism to express services on data plane. To implement classifiers in ternary-addressable memory (TCAM), each range should be encoded into multiple ternary bit strings whose number is at most linear to the width (in bits) of a represented field independently from range encoding method. In this paper we introduce a notion of a subrange allowing to represent a field range on any chosen subset of bit indices that significantly improve efficiency of classifier representations. Our analytic results are confirmed with a comprehensive evaluation study showing applicability of our approach to implement desired levels of expressiveness and scalability in packet classifiers.