TY - GEN
T1 - Optimal control of VNF deployment and scheduling
AU - Shifrin, Mark
AU - Biton, Erez
AU - Gurewitz, Omer
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/4
Y1 - 2017/1/4
N2 - Managing network-related resources involves sophisticated trade-off between flexibility, high performance standards, latency demands which adhere to service level agreements (SLAs) and cost constraints. Network functioning virtualization (NFV) opens new challenges to the remote network management which combines activation and control of virtual machines (VMs) according to variable demand. Currently, this functionality is being handled by harnessing the traditional orchestration algorithms using suboptimal heuristics. We model the problem of virtual network function (VNF) allocation by queuing system with flexible number of queues, which captures variety of constraints including queue deployment and displacement, delay cost, holding cost, scheduling reward and fine. Next, we model the system by Markov decision process (MDP) and numerically solve it to find the optimal policy. We show analytically and by simulations that the optimal policy possesses decision thresholds which depend on several parameters.1
AB - Managing network-related resources involves sophisticated trade-off between flexibility, high performance standards, latency demands which adhere to service level agreements (SLAs) and cost constraints. Network functioning virtualization (NFV) opens new challenges to the remote network management which combines activation and control of virtual machines (VMs) according to variable demand. Currently, this functionality is being handled by harnessing the traditional orchestration algorithms using suboptimal heuristics. We model the problem of virtual network function (VNF) allocation by queuing system with flexible number of queues, which captures variety of constraints including queue deployment and displacement, delay cost, holding cost, scheduling reward and fine. Next, we model the system by Markov decision process (MDP) and numerically solve it to find the optimal policy. We show analytically and by simulations that the optimal policy possesses decision thresholds which depend on several parameters.1
UR - https://www.scopus.com/pages/publications/85014283897
U2 - 10.1109/ICSEE.2016.7806110
DO - 10.1109/ICSEE.2016.7806110
M3 - Conference contribution
AN - SCOPUS:85014283897
T3 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
BT - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PB - Institute of Electrical and Electronics Engineers
T2 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Y2 - 16 November 2016 through 18 November 2016
ER -