Skip Nav Destination
ASME Press Select Proceedings
Proceedings of the International Conference on Technology Management and Innovation
By
ISBN:
9780791859612
No. of Pages:
612
Publisher:
ASME Press
Publication date:
2010
eBook Chapter
12 Application of Routing Technology in VPN Client
By
Yannian Wang
Yannian Wang
Search for other works by this author on:
Page Count:
4
-
Published:2010
Citation
Chen, Q, & Wang, Y. "Application of Routing Technology in VPN Client." Proceedings of the International Conference on Technology Management and Innovation. Ed. Xie, H. ASME Press, 2010.
Download citation file:
This paper introduces the VPN technology and the applications of VPN client, analyzes the deficiency of current VPN client, and proposes a VPN clients routing technology. By using the proposed method, VPN clients can analyze and select different access paths from different packets. Experimental results show that the method can reduce the load of VPN server, and improve the speed of client network access. Finally, the realization method of routing program is given.
Topics:
Stress
I. Introduction
II. VPN technologies
III. VPN client technologies
IV. Realization
V. Conclusions
References
This content is only available via PDF.
You do not currently have access to this chapter.
Email alerts
Related Chapters
PC-Based Stress-Measuring System for On-Line Quality Control of Tempered and Heat-Strengthened Glass
The Use of Glass in Buildings
Thermal Creep of Irradiated Zircaloy Cladding
Zirconium in the Nuclear Industry: Fourteenth International Symposium
Frank Loop Formation in Irradiated Metals in Response to Applied and Internal Stresses
Effects of Radiation on Materials
Measurement of Permeability at Elevated Stresses and Temperatures
Measurement of Rock Properties at Elevated Pressures and Temperatures
Related Articles
Experimental Research on the Responses of Neoprene Coated Cylinder Subjected to Underwater Explosions
J. Offshore Mech. Arct. Eng (February,2013)
Yield Limits of Plates at Extremely High Heat Flux
J. Heat Transfer (February,1998)
StressGAN: A Generative Deep Learning Model for Two-Dimensional Stress Distribution Prediction
J. Appl. Mech (May,2021)