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A scalable multithreaded L7-filter design for multi-core servers
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Source Symposium On Architecture For Networking And Communications Systems archive
Proceedings of the 4th ACM/IEEE Symposium on Architectures for Networking and Communications Systems table of contents
San Jose, California
SESSION: Multicore table of contents
Pages 60-68  
Year of Publication: 2008
ISBN:978-1-60558-346-4
Authors
Danhua Guo  University of California, Riverside, CA and Cisco Systems, Inc., San Jose, CA
Guangdeng Liao  University of California, Riverside, CA
Laxmi N. Bhuyan  University of California, Riverside, CA
Bin Liu  Tsinghua University, Beijing, China
Jianxun Jason Ding  Cisco Systems, Inc., San Jose, CA
Sponsors
SIGARCH: ACM Special Interest Group on Computer Architecture
SIGCOMM: ACM Special Interest Group on Data Communication
Publisher
ACM  New York, NY, USA
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ABSTRACT

L7-filter is a significant component in Linux's QoS framework that classifies network traffic based on application layer data. It enables subsequent distribution of network resources in respect to the priority of applications. Considerable research has been reported to deploy multi-core architectures for computationally intensive applications. Unfortunately, the proliferation of multi-core architectures has not helped fast packet processing due to: 1) the lack of efficient parallelism in legacy network programs, and 2) the non-trivial configuration for scalable utilization on multi-core servers.

In this paper, we propose a highly scalable parallelized L7-filter system architecture with affinity-based scheduling on a multi-core server. We start with an analytical study of the system architecture based on an offline design. Similar to Receive Side Scaling (RSS) in the NIC, we develop a model to explore the connection level parallelism in L7-filter and propose an affinity-based scheduler to optimize system scalability. Performance results show that our optimized L7-filter has superior scalability over the naive multithreaded version. It improves system performance by about 50% when all the cores are deployed.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Danhua Guo: colleagues
Guangdeng Liao: colleagues
Laxmi N. Bhuyan: colleagues
Bin Liu: colleagues
Jianxun Jason Ding: colleagues