Autonomic resource arbitration and service-continuable network function migration along service function chains

Abstract

Service function (SF) chaining enables network operators and infrastructure providers to flexibly orchestrate virtualized network functions (NFs) at various places on software defined virtual networks. Meanwhile, with the rapid prevalence of Internet-of-Things (IoT) applications and mobile network services, quality-of-service (QoS) requirements are getting increasingly diverse and network traffic volume and pattern are varying rapidly. The time-varying network traffic requires the infrastructure provider to dynamically adjust the appropriate amount of computational resources (e.g. CPU) assigned to an NF to efficiently process the traffic in the SF chaining infrastructure. In this paper, to adequately allocate CPU resources (available in limited amount) to virtual networks (VNs) requiring to satisfy diverse QoS levels in response to traffic variation without manual operations, we propose autonomic arbitration of CPU resources along SF chains which combines two CPU resource adjustment methods: The first method performs resource arbitration among VNs within a node, and the second method migrates NFs among nodes within a VN. These methods autonomically adjust the amount of CPU resource allocated to each NF so that over utilization of CPU can be avoided as much as possible. Moreover, during the autonomic resource adjustments in SF chains, the proposed methods are able to maintain communication paths on the substrate network so that the service is not interrupted at all. We also propose differentiated resource allocation for QoS differentiation and transferable resource searching. Through computer simulation, we verify that our autonomic resource adjustment methods can reduce the occurrence of CPU-saturation by more than 90%.