随波逐流:异步动态图的实时最大流量

Juntong Luo, Scott Sallinen, M. Ripeanu
{"title":"随波逐流:异步动态图的实时最大流量","authors":"Juntong Luo, Scott Sallinen, M. Ripeanu","doi":"10.1145/3594778.3594882","DOIUrl":null,"url":null,"abstract":"Processing graphs that evolve over time has seen renewed attention. Processing solutions on dynamic graphs (often dubbed \"graph streaming\" solutions) aim to maintain the state for a graph query as the graph evolves over time, and to timely offer a solution (approximate, or precise) when requested by the user. In this space, and in the context of shared-nothing platforms, solutions have been proposed only for relatively simple problems (e.g., BFS, SSSP, PageRank), and some are limited to incremental-only evolutions traces. Support for more complex problems remains rather unexplored. To close this gap, we present a solution for the maximum flow problem that supports both add and delete events. We build this solution on top of an event-based abstraction. Integral to this abstraction is that events tied to both graph topology changes and algorithmic maintenance are processed asynchronously, concurrently, and autonomously (i.e., without shared state). We show that our implementation provides favourable time-to-solution and scales well by evaluating it on a real-world dynamic graph with 80 million edges. We compare its performance with snapshot-based solutions both internally (with our own implementation of a shared-nothing static algorithm) and externally (with Galois, a popular shared-memory framework for static graphs).","PeriodicalId":371215,"journal":{"name":"Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Going with the Flow: Real-Time Max-Flow on Asynchronous Dynamic Graphs\",\"authors\":\"Juntong Luo, Scott Sallinen, M. Ripeanu\",\"doi\":\"10.1145/3594778.3594882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Processing graphs that evolve over time has seen renewed attention. Processing solutions on dynamic graphs (often dubbed \\\"graph streaming\\\" solutions) aim to maintain the state for a graph query as the graph evolves over time, and to timely offer a solution (approximate, or precise) when requested by the user. In this space, and in the context of shared-nothing platforms, solutions have been proposed only for relatively simple problems (e.g., BFS, SSSP, PageRank), and some are limited to incremental-only evolutions traces. Support for more complex problems remains rather unexplored. To close this gap, we present a solution for the maximum flow problem that supports both add and delete events. We build this solution on top of an event-based abstraction. Integral to this abstraction is that events tied to both graph topology changes and algorithmic maintenance are processed asynchronously, concurrently, and autonomously (i.e., without shared state). We show that our implementation provides favourable time-to-solution and scales well by evaluating it on a real-world dynamic graph with 80 million edges. We compare its performance with snapshot-based solutions both internally (with our own implementation of a shared-nothing static algorithm) and externally (with Galois, a popular shared-memory framework for static graphs).\",\"PeriodicalId\":371215,\"journal\":{\"name\":\"Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)\",\"volume\":\"26 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3594778.3594882\",\"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 6th Joint Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3594778.3594882","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

随着时间的推移,对图形的处理重新引起了人们的关注。动态图的处理解决方案(通常被称为“图流”解决方案)旨在维护图查询的状态,因为图随着时间的推移而演变,并在用户请求时及时提供解决方案(近似或精确)。在这个领域,在无共享平台的背景下,只针对相对简单的问题(例如,BFS、SSSP、PageRank)提出了解决方案,有些解决方案仅限于增量式的进化轨迹。对更复杂问题的支持仍然相当未被探索。为了缩小这一差距,我们提出了一个支持添加和删除事件的最大流问题的解决方案。我们在基于事件的抽象之上构建此解决方案。这个抽象的整体是,与图拓扑变化和算法维护相关的事件被异步、并发和自主地处理(即,没有共享状态)。我们证明了我们的实现提供了有利的时间到解决方案,并通过在具有8000万条边的现实世界动态图上进行评估来很好地扩展。我们将其性能与基于快照的解决方案进行了内部(使用我们自己的无共享静态算法实现)和外部(使用Galois,一种流行的用于静态图形的共享内存框架)的比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Going with the Flow: Real-Time Max-Flow on Asynchronous Dynamic Graphs
Processing graphs that evolve over time has seen renewed attention. Processing solutions on dynamic graphs (often dubbed "graph streaming" solutions) aim to maintain the state for a graph query as the graph evolves over time, and to timely offer a solution (approximate, or precise) when requested by the user. In this space, and in the context of shared-nothing platforms, solutions have been proposed only for relatively simple problems (e.g., BFS, SSSP, PageRank), and some are limited to incremental-only evolutions traces. Support for more complex problems remains rather unexplored. To close this gap, we present a solution for the maximum flow problem that supports both add and delete events. We build this solution on top of an event-based abstraction. Integral to this abstraction is that events tied to both graph topology changes and algorithmic maintenance are processed asynchronously, concurrently, and autonomously (i.e., without shared state). We show that our implementation provides favourable time-to-solution and scales well by evaluating it on a real-world dynamic graph with 80 million edges. We compare its performance with snapshot-based solutions both internally (with our own implementation of a shared-nothing static algorithm) and externally (with Galois, a popular shared-memory framework for static graphs).
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Better Distributed Graph Query Planning With Scouting Queries Fast Synthetic Data-Aware Log Generation for Temporal Declarative Models Future-Time Temporal Path Queries Going with the Flow: Real-Time Max-Flow on Asynchronous Dynamic Graphs The Commercial Side of Graph Analytics: Big Uses, Big Mistakes, Big Opportunities
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1