{"title":"The Analysis of Kademlia for Random IDs","authors":"Xing Shi Cai, L. Devroye","doi":"10.1080/15427951.2015.1051674","DOIUrl":null,"url":null,"abstract":"Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/uinm. Kademlia is thede factostandard searching algorithm for P2P (peer-to-peer) networks on the Internet. In our earlier work, we introduced two slightly different models for Kademlia and studied how many steps it takes to search for a target node by using Kademlia’s searching algorithm. The first model, in which nodes of the network are labeled with deterministic IDs, was discussed in that article. In the second, the Random ID Model, in which nodes are labeled with random IDs, was only briefly mentioned. Refined results with detailed proofs for this model are given in this article. Our analysis shows that, with high probability, it takes about clog n steps to locate any node, where n is the total number of nodes in the network and c is a constant that does not depend on n.","PeriodicalId":38105,"journal":{"name":"Internet Mathematics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/15427951.2015.1051674","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Internet Mathematics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/15427951.2015.1051674","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Mathematics","Score":null,"Total":0}
引用次数: 6
Abstract
Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/uinm. Kademlia is thede factostandard searching algorithm for P2P (peer-to-peer) networks on the Internet. In our earlier work, we introduced two slightly different models for Kademlia and studied how many steps it takes to search for a target node by using Kademlia’s searching algorithm. The first model, in which nodes of the network are labeled with deterministic IDs, was discussed in that article. In the second, the Random ID Model, in which nodes are labeled with random IDs, was only briefly mentioned. Refined results with detailed proofs for this model are given in this article. Our analysis shows that, with high probability, it takes about clog n steps to locate any node, where n is the total number of nodes in the network and c is a constant that does not depend on n.