Evaluation study of virtual network embedding for short-lived virtual networks

Cloud services are currently being extensively used for server hosting, data storage, scientific and research purposes. Virtualization technology is an essential element for these services. Virtualization enables the creation of multiple virtual instances on physical or hardware infrastructure. Network virtualization is a recent advancement in this field through which virtual networks can be created over real physical networks (also called as substrate networks). Such virtual networks facilitate testing and quick deployment of new technologies, better utilization of hardware and provide more flexibility to users.

A crucial element of network virtualization is the stage in which the virtual networks are created on the substrate network. This process is of critical nature as the number of virtual networks that are created on the substrate are high. Hence the placement of these virtual networks needs to be done in a strategic way. The creation of virtual networks on a substrate network is referred to as Virtual network embedding. Determining the best way to place or create multiple virtual networks on a substrate network while satisfying a given set of constraints is referred to as Virtual network embedding problem. The technique or algorithms used to solve this problem are known as virtual network embedding algorithms.

In this thesis we evaluate and compare six virtual network embedding algorithms for embedding short-lived virtual networks on substrate networks with fat-tree topology and UUNET topology. We discuss different metrics to evaluate the performance of embedding algorithms and compare the algorithms based on these metrics. In particular, we examine the probability of success of embedding a virtual network, average substrate path length and the distribution pattern of virtual networks in the substrate network for six different algorithms. The aim of this thesis work is to compare the performance of virtual network embedding algorithms, observe the nuances of the approaches that contribute to optimal results and investigate the embedding for the case of short-lived virtual networks.