{"title":"High-density model of content distribution network","authors":"C. Cameron, S. Low, D. Wei","doi":"10.1109/IDC.2002.995378","DOIUrl":null,"url":null,"abstract":"It is well known that optimal server placement is NP-hard. We present an approximate model of a content distribution network for the case when both clients and servers are dense, and propose a simple server allocation and placement algorithm based on high-rate quantization theory. The key idea is to regard the location of a request as a random variable with probability density that is proportional to the demand at that location, and the problem of server placement as source coding, i.e., to optimally map a source value (request location) to a codeword (server location) to minimize distortion (network cost). This view leads to a joint server allocation and placement algorithm that has a time-complexity that is linear in the number of users.","PeriodicalId":385351,"journal":{"name":"Final Program and Abstracts on Information, Decision and Control","volume":"os-3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2002-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Final Program and Abstracts on Information, Decision and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IDC.2002.995378","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
It is well known that optimal server placement is NP-hard. We present an approximate model of a content distribution network for the case when both clients and servers are dense, and propose a simple server allocation and placement algorithm based on high-rate quantization theory. The key idea is to regard the location of a request as a random variable with probability density that is proportional to the demand at that location, and the problem of server placement as source coding, i.e., to optimally map a source value (request location) to a codeword (server location) to minimize distortion (network cost). This view leads to a joint server allocation and placement algorithm that has a time-complexity that is linear in the number of users.