A Data Model Driven Approach to Managing Network Functions Virtualization : Aiding Network Operators in Provisioning and Configuring Network Functions

Abstract: This master’s thesis explains why certain network services are difficult to provision and configure using IT automation and cloud orchestration software. An improvement is proposed and motivated. This proposed improvement enables network operators to define a set of data models describing how to provision and interconnect a set of Virtual Network Functions (VNFs) (and possibly existing physical network functions) to form networks. Moreover, the proposed solution enables network operators to change the configuration at runtime. The work can be seen as a step towards self managing and auto scaling networks. The proposed approach is compared to a well known cloud management system (OpenStack) in order to evaluate if the proposed approach decreases the amount of time needed for network operators to design network topologies and services containing VNFs. Data is collected through observations of network operators, interviews, and experiment. Analysis of this data shows that the proposed approach can decrease the amount of time required for network operators to design network topologies and services. This applies if the network operators are already acquainted with the data modeling language YANG. The amount of time required to provision VNFs so that they respond to connections can also be decreased using the proposed approach. The proposed approach does not offer as much functionality as OpenStack, as it is limited to VNF scenarios.