COMPARISON OF HYPEREXPONENTIAL AND GROUP POISSON TRAFFIC MODELSOF MULTISERVICE COMMUNICATION NETWORKS

{"title":"COMPARISON OF HYPEREXPONENTIAL AND GROUP POISSON TRAFFIC MODELSOF MULTISERVICE COMMUNICATION NETWORKS","authors":"","doi":"10.18469/ikt.2023.21.4.03","DOIUrl":null,"url":null,"abstract":"The goal of the study was to compare hyperexponential and group Poisson flows of requests as models of burst traffic of multiservice communication networks. Various traffic models are considered for the problem of approximating the average queue created by video traffic of modern multiservice networks. Poisson and hyperexponential flows, both ordinary and group, are also considered. It is shown that group flows of both types are suitable for the task of approximating the video traffic queue. Group Poisson flows make it possible to obtain very simple analytical dependences of the average values of queues on the load factor of queuing systems and are therefore the most preferable. The inadequacy of using ordinary hyperexponential models for approximating burst flows of requests is also provided shown, since, at low values of the load factor, the dependences of the average values of queues for hyperexponential flows are practically close to zero, while the indicated dependences for burst flows have a very significant slope angle. The conclusions drawn are confirmed by the results of simulation modeling.","PeriodicalId":508406,"journal":{"name":"Infokommunikacionnye tehnologii","volume":"46 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infokommunikacionnye tehnologii","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18469/ikt.2023.21.4.03","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The goal of the study was to compare hyperexponential and group Poisson flows of requests as models of burst traffic of multiservice communication networks. Various traffic models are considered for the problem of approximating the average queue created by video traffic of modern multiservice networks. Poisson and hyperexponential flows, both ordinary and group, are also considered. It is shown that group flows of both types are suitable for the task of approximating the video traffic queue. Group Poisson flows make it possible to obtain very simple analytical dependences of the average values of queues on the load factor of queuing systems and are therefore the most preferable. The inadequacy of using ordinary hyperexponential models for approximating burst flows of requests is also provided shown, since, at low values of the load factor, the dependences of the average values of queues for hyperexponential flows are practically close to zero, while the indicated dependences for burst flows have a very significant slope angle. The conclusions drawn are confirmed by the results of simulation modeling.
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
多业务通信网络的超指数和群组泊松流量模型的比较
研究的目的是比较作为多业务通信网络突发流量模型的超指数请求流和组泊松请求流。针对近似现代多服务网络视频流量产生的平均队列问题,考虑了各种流量模型。还考虑了泊松和超指数流量,包括普通流量和群流量。结果表明,这两种类型的分组流都适用于近似视频流量队列的任务。分组泊松流可以非常简单地分析队列平均值与队列系统负载系数的关系,因此最受欢迎。此外,还显示了使用普通超指数模型来近似突发请求流的不足之处,因为在负载系数较低的情况下,超指数流的队列平均值的相关性实际上接近于零,而突发流的相关性则有一个非常显著的斜率角。模拟建模的结果证实了上述结论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
CONCEPT OF APPLICATION OF CONTROL ALGORITHMS FOR MANIPULATION ROBOTS TO PERFORM COMPLEX TECHNOLOGICAL OPERATIONS IN INDUSTRY OPTIMIZATION OF MODE PROPAGATION FOR AN EMBEDIER OF OPTICALVORTEX BEAMS BASED ON A MICRO-RING RESONATOR RECOMMENDATIONS ON THE URBAN NETWORK FOTL STRUCTURE WITH THE LOWEST POSSIBLE LEVEL OF DISTORTION OF INFORMATION AND CONTROL ILCF-SIGNALS STRUCTURE FEATURES OF MINIMUM FREQUENCY SHIFT KEYING SIGNAL MODEMS TRAFFIC ANOMALY DETECTION IN VEHICLE BUS BY RECURRENT LSTM NEURAL NETWORK
×
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