Tools
Tools
Ren, Yi 
ORCID: https://orcid.org/0000-0001-7423-6719, Huang, Tzu-Ming, Lin, Kate Ching-Ju and Tseng, Yu-Chee
(2018)
On scalable service function chaining with O(1) flowtable entries.
In:
IEEE INFOCOM 2018 - IEEE Conference on Computer Communications.
The Institute of Electrical and Electronics Engineers (IEEE), pp. 702-710.
Preview |
PDF (Accepted manuscript)
- Accepted Version
Download (245kB) | Preview |
Abstract
The emergence of Network Function Virtualization (NFV) enables flexible and agile service function chaining in a Software Defined Network (SDN). While this virtualization technology efficiently offers customization capability, it however comes with a cost of consuming precious TCAM resources. Due to this, the number of service chains that an SDN can support is limited by the flowtable size of a switch. To break this limitation, this paper presents CRT-Chain, a service chain forwarding protocol that requires only constant flowtable entries, regardless of the number of service chain requests. The core of CRT-Chain is an encoding mechanism that leverages Chinese Remainder Theorem (CRT) to compress the forwarding information into small labels. A switch does not need to insert forwarding rules for every service chain request, but only needs to conduct very simple modular arithmetic to extract the forwarding rules directly from CRT-Chain's labels attached in the header. We further incorporate prime reuse and path segmentation in CRT-Chain to reduce the header size and, hence, save bandwidth consumption. Our evaluation results show that, when a chain consists of no more than 5 functions, CRT-Chain actually generates a header smaller than the legacy 32-bit header defined in IETF. By enabling prime reuse and segmentation, CRT-Chain further reduces the total signaling overhead to a level lower than the conventional scheme, showing that CRT-Chain not only enables scalable flowtable-free chaining but also improves network efficiency.
| Item Type: | Book Section |
|---|---|
| Faculty \ School: | Faculty of Science > School of Computing Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Data Science and AI |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 24 Oct 2018 09:30 |
| Last Modified: | 10 Dec 2024 01:11 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/68608 |
| DOI: | 10.1109/INFOCOM.2018.8486396 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |