L. Alvisi, S. Dolev, Faith Ellen, I. Keidar, F. Kuhn, J. Suomela
{"title":"2019年Edsger W. Dijkstra分布式计算奖","authors":"L. Alvisi, S. Dolev, Faith Ellen, I. Keidar, F. Kuhn, J. Suomela","doi":"10.1145/3293611.3341564","DOIUrl":null,"url":null,"abstract":"The committee decided to award the 2019 Edsger W. Dijkstra Prize in Distributed Computing to Alessandro Panconesi and Aravind Srinivasan for their paper Randomized Distributed Edge Coloring via an Extension of the Chernoff-Hoeffding Bounds, SIAM Journal on Computing, volume 26, number 2, 1997, pages 350-368. A preliminary version of this paper appeared as Fast Randomized Algorithms for Distributed Edge Coloring, Proceedings of the Eleventh Annual ACM Symposium Principles of Distributed Computing (PODC), 1992, pages 251-262. The paper presents a simple synchronous algorithm in which processes at the nodes of an undirected network color its edges so that the edges adjacent to each node have different colors. It is randomized, using 1.6Δ + O(log1+ζn) colors and O(log n) rounds with high probability for any constant ζ>0, where n is the number of nodes and is the maximum degree of the nodes. This was the first nontrivial distributed algorithm for the edge coloring problem and has influenced a great deal of follow-up work. Edge coloring has applications to many other problems in distributed computing such as routing, scheduling, contention resolution, and resource allocation. In spite of its simplicity, the analysis of their edge coloring algorithm is highly nontrivial. Chernoff-Hoeffding bounds, which assume random variables to be independent, cannot be used. Instead, they develop upper bounds for sums of negatively correlated random variables, for example, which arise when sampling without replacement. More generally, they extend Chernoff-Hoeffding bounds to certain random variables they call λ-correlated. This has directly inspired more specialized concentration inequalities. The new techniques they introduced have also been applied to the analyses of important randomized algorithms in a variety of areas including optimization, machine learning, cryptography, streaming, quantum computing, and mechanism design.","PeriodicalId":153766,"journal":{"name":"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"2019 Edsger W. Dijkstra Prize in Distributed Computing\",\"authors\":\"L. Alvisi, S. Dolev, Faith Ellen, I. Keidar, F. Kuhn, J. Suomela\",\"doi\":\"10.1145/3293611.3341564\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The committee decided to award the 2019 Edsger W. Dijkstra Prize in Distributed Computing to Alessandro Panconesi and Aravind Srinivasan for their paper Randomized Distributed Edge Coloring via an Extension of the Chernoff-Hoeffding Bounds, SIAM Journal on Computing, volume 26, number 2, 1997, pages 350-368. A preliminary version of this paper appeared as Fast Randomized Algorithms for Distributed Edge Coloring, Proceedings of the Eleventh Annual ACM Symposium Principles of Distributed Computing (PODC), 1992, pages 251-262. The paper presents a simple synchronous algorithm in which processes at the nodes of an undirected network color its edges so that the edges adjacent to each node have different colors. It is randomized, using 1.6Δ + O(log1+ζn) colors and O(log n) rounds with high probability for any constant ζ>0, where n is the number of nodes and is the maximum degree of the nodes. This was the first nontrivial distributed algorithm for the edge coloring problem and has influenced a great deal of follow-up work. Edge coloring has applications to many other problems in distributed computing such as routing, scheduling, contention resolution, and resource allocation. In spite of its simplicity, the analysis of their edge coloring algorithm is highly nontrivial. Chernoff-Hoeffding bounds, which assume random variables to be independent, cannot be used. Instead, they develop upper bounds for sums of negatively correlated random variables, for example, which arise when sampling without replacement. More generally, they extend Chernoff-Hoeffding bounds to certain random variables they call λ-correlated. This has directly inspired more specialized concentration inequalities. The new techniques they introduced have also been applied to the analyses of important randomized algorithms in a variety of areas including optimization, machine learning, cryptography, streaming, quantum computing, and mechanism design.\",\"PeriodicalId\":153766,\"journal\":{\"name\":\"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 ACM Symposium on Principles of Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3293611.3341564\",\"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 Principles of Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3293611.3341564","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
委员会决定将2019年Edsger W. Dijkstra分布式计算奖授予Alessandro Panconesi和Aravind Srinivasan,以表彰他们的论文《通过Chernoff-Hoeffding边界的扩展随机分布边缘着色》,SIAM Journal on Computing, 1997年第26卷第2期,350-368页。本文的初步版本出现在“分布式边缘着色的快速随机算法”,第十一届ACM分布式计算原理研讨会论文集(PODC), 1992,第251-262页。本文提出了一种简单的同步算法,该算法在无向网络的节点上对其边缘上色,使每个节点相邻的边缘具有不同的颜色。它是随机的,使用1.6Δ + O(log1+ζn)颜色和O(log n)轮询,对于任意常数ζ>0具有高概率,其中n是节点的数量,是节点的最大程度。这是解决边缘着色问题的第一个非平凡分布算法,并对后续工作产生了很大影响。边缘着色可以应用于分布式计算中的许多其他问题,例如路由、调度、争用解决和资源分配。尽管其简单,但分析其边缘着色算法是非常重要的。不能使用假设随机变量是独立的Chernoff-Hoeffding界。相反,他们开发了负相关随机变量和的上界,例如,当没有替换采样时出现的上界。更一般地说,他们将切诺夫-霍夫丁界限扩展到他们称之为λ相关的某些随机变量。这直接激发了更专门化的集中不平等。他们引入的新技术也被应用于分析各种领域的重要随机算法,包括优化、机器学习、密码学、流、量子计算和机制设计。
2019 Edsger W. Dijkstra Prize in Distributed Computing
The committee decided to award the 2019 Edsger W. Dijkstra Prize in Distributed Computing to Alessandro Panconesi and Aravind Srinivasan for their paper Randomized Distributed Edge Coloring via an Extension of the Chernoff-Hoeffding Bounds, SIAM Journal on Computing, volume 26, number 2, 1997, pages 350-368. A preliminary version of this paper appeared as Fast Randomized Algorithms for Distributed Edge Coloring, Proceedings of the Eleventh Annual ACM Symposium Principles of Distributed Computing (PODC), 1992, pages 251-262. The paper presents a simple synchronous algorithm in which processes at the nodes of an undirected network color its edges so that the edges adjacent to each node have different colors. It is randomized, using 1.6Δ + O(log1+ζn) colors and O(log n) rounds with high probability for any constant ζ>0, where n is the number of nodes and is the maximum degree of the nodes. This was the first nontrivial distributed algorithm for the edge coloring problem and has influenced a great deal of follow-up work. Edge coloring has applications to many other problems in distributed computing such as routing, scheduling, contention resolution, and resource allocation. In spite of its simplicity, the analysis of their edge coloring algorithm is highly nontrivial. Chernoff-Hoeffding bounds, which assume random variables to be independent, cannot be used. Instead, they develop upper bounds for sums of negatively correlated random variables, for example, which arise when sampling without replacement. More generally, they extend Chernoff-Hoeffding bounds to certain random variables they call λ-correlated. This has directly inspired more specialized concentration inequalities. The new techniques they introduced have also been applied to the analyses of important randomized algorithms in a variety of areas including optimization, machine learning, cryptography, streaming, quantum computing, and mechanism design.