{"title":"LearningTuple: A packet classification scheme with high classification and high update","authors":"","doi":"10.1016/j.comnet.2024.110745","DOIUrl":null,"url":null,"abstract":"<div><p>Packet classification is widely used in network infrastructures and is the key technique that supports security and other functions. The real-time nature of network services naturally demands high classification speed, while the emerging SDN makes rule changes more flexible, thus placing higher demands on the performance of rule update in classification schemes. In this paper, Learning Tuple(LT) is proposed to achieve high classification performance for packets while maintaining the high update characteristics of tuple space-based schemes. Specifically, to solve the issue of excessive tuples and rule overlap due to merging tuples, LT iteratively divides the space by using rule overlap and hash collisions as negative feedback and applies a reinforcement learning algorithm, SARSA, at each level to ensure its reasonableness. Efficient space partitioning guides the construction of tuples, and an excellent rule mapping method called PLR is designed, which improves classification performance. Experimental results demonstrate that compared with classic and advanced classification schemes TSS, TupleMerge, MultilayerTuple, PartitionSort, HybridTSS, and TupleTree, LT achieves average classification performance improvements of 9.23x, 1.74x, 1.45x, 2.85x, 1.37x and 1.25x, as well as average update performance improvements of 1.83x, 6.75x, 1.22x, 6.16x, 1.21x, 10.66x, respectively.</p></div>","PeriodicalId":50637,"journal":{"name":"Computer Networks","volume":null,"pages":null},"PeriodicalIF":4.4000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Networks","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1389128624005772","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0
Abstract
Packet classification is widely used in network infrastructures and is the key technique that supports security and other functions. The real-time nature of network services naturally demands high classification speed, while the emerging SDN makes rule changes more flexible, thus placing higher demands on the performance of rule update in classification schemes. In this paper, Learning Tuple(LT) is proposed to achieve high classification performance for packets while maintaining the high update characteristics of tuple space-based schemes. Specifically, to solve the issue of excessive tuples and rule overlap due to merging tuples, LT iteratively divides the space by using rule overlap and hash collisions as negative feedback and applies a reinforcement learning algorithm, SARSA, at each level to ensure its reasonableness. Efficient space partitioning guides the construction of tuples, and an excellent rule mapping method called PLR is designed, which improves classification performance. Experimental results demonstrate that compared with classic and advanced classification schemes TSS, TupleMerge, MultilayerTuple, PartitionSort, HybridTSS, and TupleTree, LT achieves average classification performance improvements of 9.23x, 1.74x, 1.45x, 2.85x, 1.37x and 1.25x, as well as average update performance improvements of 1.83x, 6.75x, 1.22x, 6.16x, 1.21x, 10.66x, respectively.
期刊介绍:
Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.