A Packet-Combining Algorithm Based on SSLVPN with Hardware Acceleration Devices

Mei Zhen Wang; Feng Wu; Wei Ming Li; Ling Xiao; Jiang Yuan

doi:10.4028/www.scientific.net/AMR.660.152

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 660A Packet-Combining Algorithm Based on SSLVPN with...

A Packet-Combining Algorithm Based on SSLVPN with Hardware Acceleration Devices

Abstract:

SSLVPN is widely used in Web applications, e-commerce, teleworking, etc...Due to the need to provide security, the complex computing has been seriously upset the performance of SSLVPN, may even cause the VPN throughput rates decline by one to two orders of magnitude. To improve SSLVPN encryption performance, domestic and foreign scholars have made a number of studies. This paper focuses on the SSLVPN with hardware acceleration devices, and put forward a packet-combining algorithms，which could effectively improve encryption performance and data throughput of SSLVPN.

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Periodical:

Advanced Materials Research (Volume 660)

Pages:

152-157

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.660.152

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Online since:

February 2013

Authors:

Mei Zhen Wang, Feng Wu, Wei Ming Li, Ling Xiao, Jiang Yuan

Keywords:

Data Packets Combining, Hardware Accelerating Devices, Performance Improvement, SSLVPN

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References

[1] Alan O. Freier, Philip Karlton. The SSL Protocol Version 3. 0. Internet Draft. http: / wp. netscape. com/eng/ssl3/draft302. txt, 2004. 10.

Google Scholar

[2] G. Apostolopoulos,V. Peris,D. Saha. Transport layer security: how much does it really cost?In: Proceedings of 18th Annual Joint Conference of the IEEE Computer and Communications Societies(INFOCOM'99). New York: IEEE Press, 1999. 717~725.

DOI: 10.1109/infcom.1999.751458

Google Scholar

[3] S. Khanvilkar,A. Khokhar. Virtual private networks: an overview with performance evaluation. IEEE Communications Magazine, 2004, 42(10): 146~154.

DOI: 10.1109/mcom.2004.1341273

Google Scholar

[4] Song Mei, Jingsu Wang, Zhitang Li. A high performance VPN gateway parallel technology of information security and confidentiality of communications, 2005-06.

Google Scholar

[5] Ron Mraz. Secure Blue. An Architecture for a Scalable, Reliable High Volume SSL Internet Server. In. Proceedings of the 17th Annual Computer Security Applications Conference (ACSAC). Washington, DC: IEEE Computer Society Press, 2001. 391~398.

DOI: 10.1109/acsac.2001.991556

Google Scholar

[6] K. Jang, S. Han, S. Han, S. Moon, and K. Park. Accelerating SSL with GPUs. , SIGCOMM 2010. ACM.

DOI: 10.1145/1851182.1851250

Google Scholar

[7] Mohamed Khalil-Hani, Vishnu P. Nambiar,M. N. MarsonoHardware. Acceleration of OpenSSL cryptographic functions for high-performanc. Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on.

DOI: 10.1109/isms.2010.89

Google Scholar

[8] C. Rossbach, J. Currey, M. Silberstein, B. Ray, and E. Witchel ". Task: Operating System Abstractions to Manage GPUs as Compute.

DOI: 10.1145/2043556.2043579

Google Scholar