A compound model for TCP connection arrivals for LAN and WAN applications

Computer Networks

Volumn 40, Issue 3, 2002, Pages 319-337

  Nuzman, Carl ^a   Saniee, Iraj ^a   Sweldens, Wim ^a   Weiss, Alan ^a  

^a LUCENT TECHNOLOGIES   (United States)

Author keywords

Heavy tailed distributions; Infinite server queue; Long range dependence; Power law; Regular variation

Indexed keywords

MATHEMATICAL MODELS; NETWORK PROTOCOLS; SHOT NOISE; TELECOMMUNICATION TRAFFIC; WEIBULL DISTRIBUTION; WIDE AREA NETWORKS;

HEAVY-TAILED DISTRIBUTIONS;

LOCAL AREA NETWORKS;

EID: 0037159153     PISSN: 13891286     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1389-1286(02)00298-0     Document Type: Article

References (27)

1. Generating representative web workloads for network and server performance evaluation
2. On the nonsta-tionarity of internet traffic
3. Long-range dependence: A review
4. Self-similarity in world wide web traffic: Evidence and possible causes
5. Fundamental bounds and approximations for ATM multiplexers with applications to video teleconferencing
6. Per-formance impacts of multi-scaling in wide area TCP/IP traffic
7. Experimental queueing analysis with long-range dependent packet traffic
8. Characteristics of TCP connection arrivals
9. Dynamics of IP traffic: A study of the role of variability and the impact of control
10. Local area network traffic characteristics with implications for broadband network congestion management
11. Amplitude distribution of shot noise
12. Limit theorems for workload input models
13. On the self-similar nature of Ethernet traffic (extended version)
14. Doubly stochastic point process driven by fractal shot noise
15. Power-law shot noise
16. Self-similar error clusters in communication systems and the concept of conditional stationarity
17. Wide-area traffic: The failure of Poisson modeling
18. Mathematical analysis of random noise
19. The importance of long-range dependence of VBR video traffic in ATM traffic engineering: Myths and realities
20. Proof of a fundamental result in self similar traffic modeling
21. Self similarity and heavy tails: Structural modeling of network traffic