This paper surveys techniques for the recognition and treatment of self-similar network or internetwork traffic. Various researchers have reported traffic measurements that demonstrate considerable burstiness on a range of time scales with properties of self-similarity. Rapid technological development has widened the scope of network and Internet applications and, in turn, increased traffic volume. The exponential growth of the number of servers, as well as the number of users, causes Internet performance to be problematic as a result of the significant impact that long-range dependent traffic has on buffer requirements. Consequently, accurate and reliable measurement, analysis and control of Internet traffic are vital. The most significan...
This paper studies and discusses the presence of LRD in network traffic after classifying flows into...
IP traffic modeling and engineering is a challenging area that has attracted an extensive research e...
The global Internet has seen tremendous growth in terms of nodes and user base as well as of types ...
This paper surveys techniques for the recognition and treatment of self-similar network or internetw...
This paper surveys techniques for the recognition and treatment of self-similar network or internetw...
Many researchers have discussed the effects of heavy-tailedness in network traffic patterns and show...
INTRODUCTION Since the seminal study of Leland et al. [41] on the self-similar nature of network tr...
The optimal computer network performance models require accurate traffic models, which can capture t...
A long-held belief regarding the scaling behavior—self-similar or Long-Range Dependence (LRD) of Int...
The self-similarity properties of the considered traffic were checked on different time scales obtai...
Abstract- Last scientific publication shows that real network traffic is self-similar and its proper...
A study on Internet traffic characterization is essential in designing the next generation Internet....
Abstract: Traffic streams, sources as well as aggregated traffic flows, of-ten exhibit long-range-de...
Self-similarity and scaling phenomena have dominated Internet traffic analysis for the past decade.W...
We study the effects of bursty Internet Traffic through simulations. Both short-range dependency (SR...
This paper studies and discusses the presence of LRD in network traffic after classifying flows into...
IP traffic modeling and engineering is a challenging area that has attracted an extensive research e...
The global Internet has seen tremendous growth in terms of nodes and user base as well as of types ...
This paper surveys techniques for the recognition and treatment of self-similar network or internetw...
This paper surveys techniques for the recognition and treatment of self-similar network or internetw...
Many researchers have discussed the effects of heavy-tailedness in network traffic patterns and show...
INTRODUCTION Since the seminal study of Leland et al. [41] on the self-similar nature of network tr...
The optimal computer network performance models require accurate traffic models, which can capture t...
A long-held belief regarding the scaling behavior—self-similar or Long-Range Dependence (LRD) of Int...
The self-similarity properties of the considered traffic were checked on different time scales obtai...
Abstract- Last scientific publication shows that real network traffic is self-similar and its proper...
A study on Internet traffic characterization is essential in designing the next generation Internet....
Abstract: Traffic streams, sources as well as aggregated traffic flows, of-ten exhibit long-range-de...
Self-similarity and scaling phenomena have dominated Internet traffic analysis for the past decade.W...
We study the effects of bursty Internet Traffic through simulations. Both short-range dependency (SR...
This paper studies and discusses the presence of LRD in network traffic after classifying flows into...
IP traffic modeling and engineering is a challenging area that has attracted an extensive research e...
The global Internet has seen tremendous growth in terms of nodes and user base as well as of types ...