© The Institution of Engineering and Technology
Owing to the existence of noticeable concentrated periods of contention and idleness, self-similar traffic can greatly increase packet delay and loss probability and thus reduce system resource utilisation. The development of efficient congestion control mechanisms plays a central role in the improvement of network quality of service (QoS), in particular for real-time multimedia applications. By exploiting the property of scale-invariant burstiness and correlation inherent in self-similar traffic, the authors propose an effective congestion control scheme, named adaptive wavelet and probability-based scheme (AWP), which concurrently operates over multiple time scales. AWP adopts the extended multifractal wavelet model (EMWM) for analysing estimated traffic volume across multiple time scales. Furthermore, a new auto-correction algorithm based on Bayes’ theory for confidence analysis is employed to examine the validity of the predicted information. The analysis results can be used to enhance the adaptability of the prediction algorithm. In particular, the AWP framework can be easily extended to more than two time scales by increasing the level of wavelet transforms, which brings AWP a natural advantage in implementation and scalability. A series of simulation experiments have demonstrated that the proposed AWP scheme is superior to TCP and TFRC as it can greatly improve the QoS of multimedia data transmission while avoiding congestion collapse on the network.
References
-
-
1)
-
Chandrayana, K., Sikdar, B., Kalyanaraman, S.: `Comparative study of TCP compatible binomial congestion control schemes', Proc. IEEE HPSR, May 2002, Kobe, Japan, p. 319–323.
-
2)
-
V. Paxson ,
S. Floyd
.
Wide area traffic: the failure of Poisson modeling.
IEEE/ACM Trans. Netw.
,
3 ,
226 -
244
-
3)
-
J. Widmer ,
R. Denda ,
M. Mauve
.
A survey on TCP-friendly congestion control.
IEEE Netw. Mag.
,
3 ,
28 -
37
-
4)
-
Floyd, S., Handley, M., Padhye, J., Widmer, J.: `Equation-based congestion control for unicast applications', Proc. ACM SIGCOMM, 2000, p. 43–56.
-
5)
-
M.E. Crovella ,
A. Bestavros
.
Self-similarity in world wide web traffic: evidence and possible causes.
IEEE/ACM Trans. Netw.
,
6 ,
835 -
846
-
6)
-
McCanne, S., and Floyd, S.: ‘The LBNL network simulator, ns-2’, http://www.isi.edu/nsnam/ns/, 2004.
-
7)
-
Bansal, D., Balakrishnan, H.: `TCP-friendly congestion control for real-time streaming applications', MIT-LCS-TR-806 MIT Technical Report, 2000.
-
8)
-
W. Leland ,
M. Taqqu ,
W. Willinger ,
D. Wilson
.
On the self-similar nature of Ethernet traffic (extended version).
IEEE/ACM Trans. Netw.
,
1 ,
1 -
15
-
9)
-
P. Baldi ,
S. Brunak
.
(2001)
Bioinformatics – the machine learning approach.
-
10)
-
K.H. Cho ,
S.J. Park ,
E.H. Jung ,
S.W. Shin ,
H.H. Lee
.
End-to-end rate-based congestion control using EWMA for multicast services in IP networks.
IEE Proc., Commun.
,
5 ,
668 -
672
-
11)
-
N.R. Sastry ,
S.S. Lam
.
CYRF: a theory of window-based unicast congestion control.
IEEE/ACM Trans. Netw.
,
2 ,
330 -
342
-
12)
-
Bansal, D., Balakrishnan, H.: `Binomial congestion control algorithms', Proc. IEEE INFOCOM, April 2001, p. 631–640.
-
13)
-
R.H. Riedi ,
M.S. Crouse ,
V.J. Ribeiro ,
R. Baraniuk
.
A multifractal wavelet model with applications to network traffic.
IEEE Trans. Inf. Theory
,
3 ,
992 -
1018
-
14)
-
T. Tuan ,
K. Park
.
Multiple time scale congestion control for self-similar network traffic.
Perform. Eval.
,
359 -
386
-
15)
-
Q. Zhang ,
W.W. Zhu ,
Y.Q. Zhang
.
Network-adaptive rate control and unequal loss protection with TCP-friendly protocol for scalable video over internet.
J. VLSI Signal Process. Syst. Signal Image Video Technol.
,
109 -
112
-
16)
-
Z. Sahinoglu ,
S. Tekinay
.
On multimedia networks: self-similar traffic and network performance.
IEEE Commun. Mag.
,
1 ,
48 -
52
-
17)
-
W. Stallings
.
(2002)
High-speed networks and internets: performance and quality of service.
-
18)
-
P. Abry ,
P. Flandrin ,
M.S. Taqqu ,
D. Veitch ,
K. Park ,
W. Willinger
.
(2000)
Wavelets for the analysis, estimation, and synthesis of scaling data, Self similar network traffic analysis and performance evaluation.
-
19)
-
Ribeiro, V., Coates, M., Riedi, R., Sarvotham, S.: `Multifractal cross-traffic estimation', Proc. ITC Specialist Seminar on IP Traffic Measurement, Modeling and Management, September 2000, Monterey, CA, 15(1–10).
-
20)
-
Weisstein, E.: ‘Bayes' theorem, beta distribution’. From MathWorld–A Wolfram Web Resource, http://mathworld.wolfram.com/BayesTheorem.html, 2004.
-
21)
-
Handley, M., Floyd, S., Padhye, J., Widmer, J.: RFC 3448, Jan. 2003.
http://iet.metastore.ingenta.com/content/journals/10.1049/ip-com_20050671
Related content
content/journals/10.1049/ip-com_20050671
pub_keyword,iet_inspecKeyword,pub_concept
6
6