TY - GEN
T1 - Resilient Virtual Network Function Placement Model Based on Recovery Time Objectives
AU - Hyodo, Naoki
AU - Sato, Takehiro
AU - Shinkuma, Ryoichi
AU - Oki, Eiji
N1 - Funding Information:
This work was supported in part by JSPS KAKENHI Grant Numbers 18H03230 and 19K14980, Japan.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/5
Y1 - 2020/5
N2 - This paper proposes a virtual network function (VNF) placement model for service chaining that minimizes the cost of using computation resources when no failure occurs while guaranteeing recovery against any single facility node failure within the recovery time objective (RTO) defined for each service. The proposed model adaptively allocates computation resources to each service under its RTO constraint. The proposed model introduces two sharing methods of computation resources among multiple service chains. The first method allows sharing a virtual machine (VM) where a VNF is scheduled to run after a failure, which contributes to suppressing the number of VMs reserved in preparation for a failure. The second method allows sharing computation capability used for VMs, which prevents unnecessary VNF scale-up that requires additional computation resources. A simulation study verifies that the proposed model reduces the cost of using computation resources compared to comparative models.
AB - This paper proposes a virtual network function (VNF) placement model for service chaining that minimizes the cost of using computation resources when no failure occurs while guaranteeing recovery against any single facility node failure within the recovery time objective (RTO) defined for each service. The proposed model adaptively allocates computation resources to each service under its RTO constraint. The proposed model introduces two sharing methods of computation resources among multiple service chains. The first method allows sharing a virtual machine (VM) where a VNF is scheduled to run after a failure, which contributes to suppressing the number of VMs reserved in preparation for a failure. The second method allows sharing computation capability used for VMs, which prevents unnecessary VNF scale-up that requires additional computation resources. A simulation study verifies that the proposed model reduces the cost of using computation resources compared to comparative models.
KW - failure recovery
KW - integer linear programming
KW - recovery time objective
KW - service chaining
KW - virtual network function
UR - http://www.scopus.com/inward/record.url?scp=85085699227&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85085699227&partnerID=8YFLogxK
U2 - 10.1109/HPSR48589.2020.9098968
DO - 10.1109/HPSR48589.2020.9098968
M3 - Conference contribution
AN - SCOPUS:85085699227
T3 - IEEE International Conference on High Performance Switching and Routing, HPSR
BT - 2020 IEEE 21st International Conference on High Performance Switching and Routing, HPSR 2020
PB - IEEE Computer Society
T2 - 21st IEEE International Conference on High Performance Switching and Routing, HPSR 2020
Y2 - 11 May 2020 through 14 May 2020
ER -