DTIC MdM Strategic Program: Artificial and Natural Intelligence for ICT and beyond

Rankothge W, Ma J, Le F, Russo A, Lobo J. Towards making network function virtualization a cloud computing service. Proceedings of the 2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)

By allowing network functions to be virtualized and run on commodity hardware, NFV enables new properties (e.g., elastic scaling), and new service models for Service Providers, Enterprises, and Telecommunication Service Providers. However, for NFV to be offered as a service, several research problems still need to be addressed. In this paper, we focus and propose a new service chaining algorithm. Existing solutions suffer two main limitations: First, existing proposals often rely on mixed Integer Linear Programming to optimize VM allocation and network management, but our experiments show that such approach is too slow taking hours to find a solution. Second, although existing proposals have considered the VM placement and network configuration jointly, they frequently assume the network configuration cannot be changed. Instead, we believe that both computing and network resources should be able to be updated concurrently for increased flexibility and to satisfy SLA and Qos requirements. As such, we formulate and propose a Genetic Algorithm based approach to solve the VM allocation and network management problem. We built an experimental NFV platform, and run a set of experiments. The results show that our proposed GA approach can compute configurations to to three orders of magnitude faster than traditional solutions.

Additional material:

• Datasets for the Evaluation of Virtualized Network Functions Resource Allocation Algorithms. This repository contains all the details about how we modelled general data into the specific data we wanted, with along the software we used and the assumptions we made during the data modelling process. Using this data and programs, the evaluation results presented in our publications can be easily reproduced.