TY - GEN
T1 - Chameleon
T2 - 16th ACM Conference on Emerging Networking Experiment and Technologies, CoNEXT 2020
AU - Van Bemten, Amaury
AU - Derić, Nemanja
AU - Varasteh, Amir
AU - Schmid, Stefan
AU - Mas-Machuca, Carmen
AU - Blenk, Andreas
AU - Kellerer, Wolfgang
N1 - Publisher Copyright:
© 2020 ACM.
PY - 2020/11/23
Y1 - 2020/11/23
N2 - This paper presents Chameleon, a cloud network providing both predictable latency and high utilization, typically two conflicting goals, especially in multi-tenant datacenters. Chameleon exploits routing flexibilities available in modern communication networks to dynamically adapt toward the demand, and uses network calculus principles along individual paths. More specifically, Chameleon employs source routing on the "queue-level topology", a network abstraction that accounts for the current states of the network queues and, hence, the different delays of different paths. Chameleon is based on a simple greedy algorithm and can be deployed at the edge; it does not require any modifications of network devices. We implement and evaluate Chameleon in simulations and a real testbed. Compared to state-of-the-art, we find that Chameleon can admit and embed significantly, i.e., up to 15 times more flows, improving network utilization while meeting strict latency guarantees.
AB - This paper presents Chameleon, a cloud network providing both predictable latency and high utilization, typically two conflicting goals, especially in multi-tenant datacenters. Chameleon exploits routing flexibilities available in modern communication networks to dynamically adapt toward the demand, and uses network calculus principles along individual paths. More specifically, Chameleon employs source routing on the "queue-level topology", a network abstraction that accounts for the current states of the network queues and, hence, the different delays of different paths. Chameleon is based on a simple greedy algorithm and can be deployed at the edge; it does not require any modifications of network devices. We implement and evaluate Chameleon in simulations and a real testbed. Compared to state-of-the-art, we find that Chameleon can admit and embed significantly, i.e., up to 15 times more flows, improving network utilization while meeting strict latency guarantees.
KW - latency
KW - network calculus
KW - predictability
KW - reconfigurations
UR - http://www.scopus.com/inward/record.url?scp=85097633680&partnerID=8YFLogxK
U2 - 10.1145/3386367.3432879
DO - 10.1145/3386367.3432879
M3 - Conference contribution
AN - SCOPUS:85097633680
T3 - CoNEXT 2020 - Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies
SP - 451
EP - 465
BT - CoNEXT 2020 - Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies
PB - Association for Computing Machinery, Inc
Y2 - 1 December 2020 through 4 December 2020
ER -