TY - GEN
T1 - CLEO
T2 - 15th International Conference on Emerging Networking EXperiments and Technologies, CoNEXT 2019 - Part of CoNEXT 2019
AU - Jin, Heesang
AU - Kang, Minkoo
AU - Yang, Gyeongsik
AU - Yoo, Chuck
N1 - Funding Information:
We would like to thank the anonymous reviewers for their insightful comments. This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2015-0-00288, Research of Network Virtualization Platform and Service for SDN 2.0 Realization, and No. 2015-0-00280, (SW Starlab) Next generation cloud infra-software toward the guarantee of performance and security SLA).
Publisher Copyright:
© 2019 held by the owner/author(s).
PY - 2019/12/9
Y1 - 2019/12/9
N2 - In this paper, we propose CLEO, which is a machine learning approach to equal-cost multipath routing (ECMP) schemes to distribute and balance traffic. ECMP-based traffic load-balancing is widely practiced by datacenters, but hash collision resulting from skewed ECMP hashing makes it difficult to achieve the desired throughputs over paths. Various solutions have been proposed to overcome the performance degradation caused by hash collision, but most of these solutions require modifying packet headers or replacing switches. To solve this problem, CLEO builds a neural-network model that characterizes the ECMP scheme of a switch. The proof-of-concept evaluation shows that CLEO improves the root mean square error fourfold between the desired and real path throughputs.
AB - In this paper, we propose CLEO, which is a machine learning approach to equal-cost multipath routing (ECMP) schemes to distribute and balance traffic. ECMP-based traffic load-balancing is widely practiced by datacenters, but hash collision resulting from skewed ECMP hashing makes it difficult to achieve the desired throughputs over paths. Various solutions have been proposed to overcome the performance degradation caused by hash collision, but most of these solutions require modifying packet headers or replacing switches. To solve this problem, CLEO builds a neural-network model that characterizes the ECMP scheme of a switch. The proof-of-concept evaluation shows that CLEO improves the root mean square error fourfold between the desired and real path throughputs.
UR - http://www.scopus.com/inward/record.url?scp=85077961343&partnerID=8YFLogxK
U2 - 10.1145/3360468.3366768
DO - 10.1145/3360468.3366768
M3 - Conference contribution
AN - SCOPUS:85077961343
T3 - CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019
SP - 1
EP - 3
BT - CoNEXT 2019 Companion - Proceedings of the 15th International Conference on Emerging Networking EXperiments and Technologies, Part of CoNEXT 2019
PB - Association for Computing Machinery, Inc
Y2 - 9 December 2019 through 12 December 2019
ER -