内容分发网络中镜像服务器位置选择的建模

Peter Hillmann, Tobias Uhlig, G. Rodosek, O. Rose
{"title":"内容分发网络中镜像服务器位置选择的建模","authors":"Peter Hillmann, Tobias Uhlig, G. Rodosek, O. Rose","doi":"10.1109/BigDataCongress.2016.68","DOIUrl":null,"url":null,"abstract":"For a provider of a Content Delivery Network (CDN), the location selection of mirror servers is a complex optimization problem. Generally, the objective is to place the nodes centralized such that all customers have convenient access to the service according to their demands. It is an instance of the k-center problem, which is proven to be NP-hard. Determining reasonable server locations directly influences run time effects and future service costs. We model, simulate, and optimize the properties of a content delivery network. Specifically, considering the server locations in a network infrastructure with prioritized customers and weighted connections. A simulation model for the servers is necessary to analyze the caching behavior in accordance to the targeted customer requests. We analyze the problem and compare different optimization strategies. For our simulation, we employ various realistic scenarios and evaluate several performance indicators. Our new optimization approach shows a significant improvement. The presented results are generally applicable to other domains with k-center problems, e.g., the placement of military bases, the planning and placement of facility locations, or data mining.","PeriodicalId":407471,"journal":{"name":"2016 IEEE International Congress on Big Data (BigData Congress)","volume":" 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Modeling the Location Selection of Mirror Servers in Content Delivery Networks\",\"authors\":\"Peter Hillmann, Tobias Uhlig, G. Rodosek, O. Rose\",\"doi\":\"10.1109/BigDataCongress.2016.68\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"For a provider of a Content Delivery Network (CDN), the location selection of mirror servers is a complex optimization problem. Generally, the objective is to place the nodes centralized such that all customers have convenient access to the service according to their demands. It is an instance of the k-center problem, which is proven to be NP-hard. Determining reasonable server locations directly influences run time effects and future service costs. We model, simulate, and optimize the properties of a content delivery network. Specifically, considering the server locations in a network infrastructure with prioritized customers and weighted connections. A simulation model for the servers is necessary to analyze the caching behavior in accordance to the targeted customer requests. We analyze the problem and compare different optimization strategies. For our simulation, we employ various realistic scenarios and evaluate several performance indicators. Our new optimization approach shows a significant improvement. The presented results are generally applicable to other domains with k-center problems, e.g., the placement of military bases, the planning and placement of facility locations, or data mining.\",\"PeriodicalId\":407471,\"journal\":{\"name\":\"2016 IEEE International Congress on Big Data (BigData Congress)\",\"volume\":\" 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 IEEE International Congress on Big Data (BigData Congress)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BigDataCongress.2016.68\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE International Congress on Big Data (BigData Congress)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BigDataCongress.2016.68","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 7

摘要

对于内容分发网络(CDN)的提供商来说,镜像服务器的位置选择是一个复杂的优化问题。通常,目标是将节点集中放置,以便所有客户能够根据其需求方便地访问服务。这是k中心问题的一个实例,它被证明是np困难的。确定合理的服务器位置直接影响运行时效果和未来的服务成本。我们对内容交付网络的属性进行建模、模拟和优化。具体来说,考虑服务器在具有优先客户和加权连接的网络基础设施中的位置。为了根据目标客户请求分析缓存行为,服务器的仿真模型是必要的。我们对问题进行了分析,并比较了不同的优化策略。对于我们的模拟,我们采用了各种现实场景并评估了几个性能指标。我们的新优化方法显示了显著的改进。所提出的结果通常适用于其他具有k-中心问题的领域,例如,军事基地的安置,设施位置的规划和安置,或数据挖掘。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modeling the Location Selection of Mirror Servers in Content Delivery Networks
For a provider of a Content Delivery Network (CDN), the location selection of mirror servers is a complex optimization problem. Generally, the objective is to place the nodes centralized such that all customers have convenient access to the service according to their demands. It is an instance of the k-center problem, which is proven to be NP-hard. Determining reasonable server locations directly influences run time effects and future service costs. We model, simulate, and optimize the properties of a content delivery network. Specifically, considering the server locations in a network infrastructure with prioritized customers and weighted connections. A simulation model for the servers is necessary to analyze the caching behavior in accordance to the targeted customer requests. We analyze the problem and compare different optimization strategies. For our simulation, we employ various realistic scenarios and evaluate several performance indicators. Our new optimization approach shows a significant improvement. The presented results are generally applicable to other domains with k-center problems, e.g., the placement of military bases, the planning and placement of facility locations, or data mining.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信