TY - GEN
T1 - Reducing data center resource over-provisioning through dynamic load management for virtualized network functions
AU - Oeldemann, Andreas
AU - Wild, Thomas
AU - Herkersdorf, Andreas
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Network Function Virtualization aims at replacing specialized hardware network appliances by commodity servers. In this paper, we address sub-second variations in data center network workloads, which place highly volatile processing demands on the servers. This makes an efficient dimensioning of the hardware resources dedicated to network function execution challenging. Based on the observation that short-term peak workloads typically do not hit all machines at exactly the same time, we propose to enable the servers to reuse under-utilized resources of their peers by selectively redirecting packets when local resources are exhausted. To satisfy line rate performance demands, we present a hardware load management layer, which is located in the ingress path of each server. Our simulative evaluation shows that the load management layer can reduce the hardware resources required for network function execution by up to 24% while maintaining network throughput and latency performance. Especially in large data centers, these resource savings can significantly reduce network expenses.
AB - Network Function Virtualization aims at replacing specialized hardware network appliances by commodity servers. In this paper, we address sub-second variations in data center network workloads, which place highly volatile processing demands on the servers. This makes an efficient dimensioning of the hardware resources dedicated to network function execution challenging. Based on the observation that short-term peak workloads typically do not hit all machines at exactly the same time, we propose to enable the servers to reuse under-utilized resources of their peers by selectively redirecting packets when local resources are exhausted. To satisfy line rate performance demands, we present a hardware load management layer, which is located in the ingress path of each server. Our simulative evaluation shows that the load management layer can reduce the hardware resources required for network function execution by up to 24% while maintaining network throughput and latency performance. Especially in large data centers, these resource savings can significantly reduce network expenses.
UR - http://www.scopus.com/inward/record.url?scp=85014846211&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-54999-6_18
DO - 10.1007/978-3-319-54999-6_18
M3 - Conference contribution
AN - SCOPUS:85014846211
SN - 9783319549989
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 234
EP - 247
BT - Architecture of Computing Systems – ARCS 2017 - 30th International Conference, Proceedings
A2 - Schulz, Martin
A2 - Pionteck, Thilo
A2 - Karl, Wolfgang
A2 - Inoue, Koji
A2 - Knoop, Jens
PB - Springer Verlag
T2 - 30th International Conference on Architecture of Computing Systems, ARCS 2017
Y2 - 3 April 2017 through 6 April 2017
ER -