  Network Function Virtualization (NFV) is a new paradigm, enabling service
innovation through virtualization of traditional network functions located
flexibly in the network in form of Virtual Network Functions (VNFs). Since VNFs
can only be placed onto servers located in networked data centers, which is the
NFV's salient feature, the traffic directed to these data center areas has
significant impact on network load balancing. Network load balancing can be
even more critical for an ordered sequence of VNFs, also known as Service
Function Chains (SFCs), a common cloud and network service approach today. To
balance the network load, VNF's can be placed in a smaller cluster of servers
in the network thus minimizing the distance to the data center. The
optimization of the placement of these clusters is a challenge as also other
factors need to be considered, such as the resource utilization. To address
this issue, we study the problem of VNF placement with replications, and
especially the potential of VNFs replications to help load balance the network.
We design and compare three optimization methods, including Linear Programing
(LP) model, Genetic Algorithm (GA) and Random Fit Placement Algorithm (RFPA)
for the allocation and replication of VNFs. Our results show that the optimum
placement and replication can significantly improve load balancing, for which
we also propose a GA heuristics applicable to larger networks.
