TY - CONF
T1 - Distributed Online Resource Allocation Using Congestion Game for 5G Virtual Network Services
AU - Bi, Yu
AU - Bunyakitanon, Monchai
AU - Uniyal, Navdeep
AU - Bravalheri, Anderson C
AU - Muqaddas, Abubakar Siddique
AU - Nejabati, Reza
AU - Simeonidou, Dimitra
PY - 2020/2/27
Y1 - 2020/2/27
N2 - To meet the challenge of flexible and dynamic resource provisioning for massive and various network services, we investigate the network function virtualization-resource allocation problem in the 5G network. For the first time, this problem is modelled as the congestion game to capture the effects of resource congestion on packet processing latency, optical-to-electronic and electronic-to-optical conversion latency. All the network service requests received at the same time are players trying to minimise their own end-to-end latency and resource consumption cost. A distributed online algorithm is designed and simulation results show that it can achieve 100% service acceptance ratio while the baseline algorithm cannot. If lower weighted resource consumption cost is set for 1ms and 5ms services, more such services will be routed to edge nodes and network operators will earn more. An experiment for network services with different packet sizes is carried out, and results prove that the proposed algorithm converges to Nash Equilibrium in 40 seconds and the latency requirements are all satisfied if the packet size is small.
AB - To meet the challenge of flexible and dynamic resource provisioning for massive and various network services, we investigate the network function virtualization-resource allocation problem in the 5G network. For the first time, this problem is modelled as the congestion game to capture the effects of resource congestion on packet processing latency, optical-to-electronic and electronic-to-optical conversion latency. All the network service requests received at the same time are players trying to minimise their own end-to-end latency and resource consumption cost. A distributed online algorithm is designed and simulation results show that it can achieve 100% service acceptance ratio while the baseline algorithm cannot. If lower weighted resource consumption cost is set for 1ms and 5ms services, more such services will be routed to edge nodes and network operators will earn more. An experiment for network services with different packet sizes is carried out, and results prove that the proposed algorithm converges to Nash Equilibrium in 40 seconds and the latency requirements are all satisfied if the packet size is small.
M3 - Conference Paper
ER -