Virtualization is central to cloud computing systems. It abstracts computing resources to be shared among multiple virtual machines (VMs) that can be easily managed to run multiple applications and services. To benefit from the advantages of cloud computing, and to cope with increasing traffic demands, telecom operators have adopted cloud computing. Telecom services and applications are, however, characterized by real-time responsiveness, strict end-to-end latency, and high reliability. Due to the inherent overhead of virtualization, the network performance of applications and services can be degraded. To improve the performance of emerging applications and services that demand stringent end-to-end latency, and to understand the network performance bottleneck of virtualization, a comprehensive performance measurement and analysis is required. To this end, we conducted controlled and detailed experiments to understand the impact of virtualization on end-to-end latency and the performance of transport protocols in a virtualized environment. We also provide a packet delay breakdown in the virtualization layer which helps in the optimization of hypervisor components. Our experimental results indicate that the end-to-end latency and packet delay in the virtualization layer are increased with co-located VMs.

Network Function Virtualization (NFV) is a promising solution for telecom operators and service providers to improve business agility, by enabling a fast deployment of new services, and by making it possible for them to cope with the increasing traffic volume and service demand. NFV enables virtualization of network functions that can be deployed as virtual machines on general purpose server hardware in cloud environments, effectively reducing deployment and operational costs. To benefit from the advantages of NFV, virtual network functions (VNFs) need to be provisioned with sufficient resources and perform without impacting network quality of service (QoS). To this end, this paper proposes a model for VNFs placement and provisioning optimization while guaranteeing the latency requirements of the service chains. Our goal is to optimize resource utilization in order to reduce cost satisfying the QoS such as end- to-end latency. We extend a related VNFs placement optimization with a fine-grained latency model including virtualization overhead. The model is evaluated with a simulated network and it provides placement solutions ensuring the required QoS guarantees.