Design and Implementation of Scalable High-Performance Network Functions

Service Function Chaining (SFC) enriches the network functionalities to fulfill the increasing demand of value-added services. By leveraging SDN and NFV for SFC, it becomes possible to meet the demand fluctuation and construct a dynamic SFc. However, the integration of SDN with NFV requires packet header modifications, generates excessive network traffics, and induces additional I/O overheads for packet processing. These additional overheads result in a lower system performance, scalability, and agility. To improve the system performance, a co-optimized solution is proposed to implemented NF to achieve a better performance for software-based network functions. To improve the system scalability, a many-field packet classification is proposed to support a more complex ruleset. To improve the system agility, a network function-enabled switch is proposed to lower the network function content switching time. The experiment results show that the performance of a network function is improved by 8 times by leveraging GPU as a parallel computation platform. Moreover, the matching speed to steer network traffics with many-field ruleset is improved by 4 times with the proposed many-field packet classification algorithm. Finally, the proposed system is able to improve system bandwidth 5 times better compared the native solution and maintain the content switch time with the proposed SFC implementation using SDN and NFV.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

Campus Only Dissertation

Discipline thesis:degree_discipline

Electrical and Computer Engineering

Year

2017

Author dc:creator

• Hsieh, Cheng-Liang

• Graphics Processing Unit
• Network Function Virtualization
• Service Function Chaining
• Software-Defined Networking

Repository record dc:identifier

https://opensiuc.lib.siu.edu/dissertations/1416

OAI identifier oai:identifier

oai:opensiuc.lib.siu.edu:dissertations-2420