Recent Advances and Future Challenges for Network Function Virtualization Infrastructure

Today's enterprise networks has revolutionized by the emerging technology called Network function virtualization (NFV), which is a type of data center network architecture proposed by the European Telecommunications Standards Institute (ETSI). NFV uses virtualization techniques to implement various Network Functions (NFs) like firewall, load balancer etc. from dedicated network devices to virtualized instances in commodity servers. This virtualized instance is called as Virtual Network Function (VNF). The purpose of VNF is to process NFs in order to accomplish a specific task. Traditionally these NFs were implemented on dedicated network equipment called middleboxes. Although these middleboxes are capable of processing heavy workloads, they are expensive, inflexible and require experts to maintain them. Therefore, NFV has the potential to substitute these middleboxes with virtualized instances in cloud datacenters, and hence greatly reduces the Operational Expenditure (OPEX) and Capital Expenditure (CAPEX) of networks by making it cheaper, flexible and scalable. This talk will share the recent advances and future challenges on how to build the infrastructure for hosting and managing NFVs. In particularly, we will focus on two of the most important topics in this research direction. First is the VNF placement problem, which aims to find the best mapping decision between VNF instances and physical resources. It has significant impact to the network operation cost, and service quality, but it is also known to be a NP-hard problem. So the problem has been actively studied by the research community. The second topic is Cloud-native/Container Network Function (CNF), which aims to minimize the overhead of traditional virtualization technique for network function using container-based technologies. Hence, it has drawn growing interests from industry to build the NFV infrastructure for CNF, but many new challenges remain to be addressed and studied.