{"title":"通过全对全通信实现分布式管理信息系统","authors":"M. Ghaffari","doi":"10.1145/3087801.3087830","DOIUrl":null,"url":null,"abstract":"Computing a Maximal Independent Set (MIS) is a central problem in distributed graph algorithms. This paper presents an improved randomized distributed algorithm for congested clique model, defined as follows: Given a graph G=(V, E), initially each node knows only its neighbors. Communication happens in synchronous rounds over a complete graph, and per round each node can send O(log n) bits to each other node. We present a randomized algorithm that computes an MIS in Õ((log Δ)/(√(log n)) + 1 ) ≤ Õ(√(log Δ)) rounds of congested clique, with high probability. Here Δ denotes the maximum degree in the graph. This improves quadratically on the O(log Δ) algorithm of [Ghaffari, SODA'16]. The core technical novelty in this result is a certain local sparsification technique for MIS, which we believe to be of independent interest.","PeriodicalId":324970,"journal":{"name":"Proceedings of the ACM Symposium on Principles of Distributed Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"40","resultStr":"{\"title\":\"Distributed MIS via All-to-All Communication\",\"authors\":\"M. Ghaffari\",\"doi\":\"10.1145/3087801.3087830\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Computing a Maximal Independent Set (MIS) is a central problem in distributed graph algorithms. This paper presents an improved randomized distributed algorithm for congested clique model, defined as follows: Given a graph G=(V, E), initially each node knows only its neighbors. Communication happens in synchronous rounds over a complete graph, and per round each node can send O(log n) bits to each other node. We present a randomized algorithm that computes an MIS in Õ((log Δ)/(√(log n)) + 1 ) ≤ Õ(√(log Δ)) rounds of congested clique, with high probability. Here Δ denotes the maximum degree in the graph. This improves quadratically on the O(log Δ) algorithm of [Ghaffari, SODA'16]. The core technical novelty in this result is a certain local sparsification technique for MIS, which we believe to be of independent interest.\",\"PeriodicalId\":324970,\"journal\":{\"name\":\"Proceedings of the ACM Symposium on Principles of Distributed Computing\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"40\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM Symposium on Principles of Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3087801.3087830\",\"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 ACM Symposium on Principles of Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3087801.3087830","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Computing a Maximal Independent Set (MIS) is a central problem in distributed graph algorithms. This paper presents an improved randomized distributed algorithm for congested clique model, defined as follows: Given a graph G=(V, E), initially each node knows only its neighbors. Communication happens in synchronous rounds over a complete graph, and per round each node can send O(log n) bits to each other node. We present a randomized algorithm that computes an MIS in Õ((log Δ)/(√(log n)) + 1 ) ≤ Õ(√(log Δ)) rounds of congested clique, with high probability. Here Δ denotes the maximum degree in the graph. This improves quadratically on the O(log Δ) algorithm of [Ghaffari, SODA'16]. The core technical novelty in this result is a certain local sparsification technique for MIS, which we believe to be of independent interest.