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The concept of Network Function Virtualization (NFV) has been introduced as a new paradigm in the recent few years. NFV offers a number of benefits including significantly increased maintainability and reduced deployment overhead. Several works have been done to optimize deployment (also called embedding) of virtual network functions (VNFs). However, no work to date has looked into optimizing the selection of cloud instances for a given VNF and its specific requirements. In this paper, we evaluate the performance of VNFs when embedded on different Amazon EC2 cloud instances. Specifically, we evaluate three VNFs (firewall, IDS, and NAT) in terms of arrival packet rate, resources utilization, and packet loss.Our results indicate that performance varies across instance types, departing from the intuition of "you get what you pay for" with cloud instances. We also find out that CPU is the critical resource for the tested VNFs, although their peak packet processing capacities differ considerably from each other. Finally, based on the obtained results, we identify key research challenges related to VNF instance selection and service chain provisioning.

With the growing deployment of emergent technologies like software-defined networking, network services are expected to be revolutionized. In this paper, we investigate offering Service Function Chains as a Service (SFCaaS) in NFV environments. We describe the potential business model to offer such a service and then we address the service function chain provisioning and resource allocation problem. As the chain is deployed thanks to virtual machines (i.e., instances) and links, we conduct first a detailed study of the costs of Amazon EC2 instances with respect to their location, size, type and performance. Afterwards, we address the resource allocation problem for service function chains from the SFC provider's perspective. We formulate the problem as an integer linear program aiming at reducing operational costs of the service function chains (i.e., costs of virtual machine instances and links, and synchronization among the instances). To address large-scale instances of the problem, we also propose a new heuristic algorithm to reduce operational costs taking into account the conducted study of the costs of Amazon EC2 instances. We show through extensive simulations that the proposed heuristic significantly reduces operational costs compared to a baseline algorithm inspired by the existing literature.

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