Performances of OpenFlow-Based Software- Defined Networks: An overview <s> B. Virtualization-Based Improvements <s> 1. SLICED PROGRAMMABLE NETWORKS OpenFlow [4] has been demonstrated as a way for researchers to run networking experiments in their production network. Last year, we demonstrated how an OpenFlow controller running on NOX [3] could move VMs seamlessly around an OpenFlow network [1]. While OpenFlow has potential [2] to open control of the network, only one researcher can innovate on the network at a time. What is required is a way to divide, or slice, network resources so that researchers and network administrators can use them in parallel. Network slicing implies that actions in one slice do not negatively affect other slices, even if they share the same underlying physical hardware. A common network slicing technique is VLANs. With VLANs, the administrator partitions the network by switch port and all traffic is mapped to a VLAN by input port or explicit tag. This coarse-grained type of network slicing complicates more interesting experiments such as IP mobility or wireless handover. Here, we demonstrate FlowVisor, a special purpose OpenFlow controller that allows multiple researchers to run experiments safely and independently on the same production OpenFlow network. To motivate FlowVisor’s flexibility, we demonstrate four network slices running in parallel: one slice for the production network and three slices running experimental code (Figure 1). Our demonstration runs on real network hardware deployed on our production network at Stanford and a wide-area test-bed with a mix of wired and wireless technologies. <s> BIB001 </s> Performances of OpenFlow-Based Software- Defined Networks: An overview <s> B. Virtualization-Based Improvements <s> Network virtualization gives each "tenant" in a data center its own network topology and control over its traffic flow. Software-defined networking offers a standard interface between controller applications and switch-forwarding tables, and is thus a natural platform for network virtualization. Yet, supporting numerous tenants with different topologies and controller applications raises scalability challenges. The FlowN architecture gives each tenant the illusion of its own address space, topology, and controller, and leverages database technology to efficiently store and manipulate mappings between virtual networks and physical switches. <s> BIB002
Network virtualization is a particular abstraction of a physical network that allows supporting multiple logical networks to run on a common physical substrate. SDN effectively separates the data plane and the control plane, whereas virtual networks separate logical and physical networks. The two concepts are certainly distinct, but SDN is a useful tool for implementing virtual networks. To benefit from SDN and network virtualization, and improve the performance of an SDN network, researchers add new layer between control-plane and data-plane called Flowvisor BIB001 . Flowvisor basically virtualizes network control by letting experimental traffic run in parallel on the production network with the real user, and real production traffic. Flowvisor specifies some subset of the traffic that he is willing to let run over that experimental network control. This is achieved using a concept called flow space, where some subset of traffic flows, based on IP address, port, might be specified as being controlled by an experimental network controller, as opposed to the production network control. Whereas FlowVisor can be compared to a full virtualization technology, FlowN BIB002 is a virtualization solution where each tenant (instance) has the illusion of having its own address space, topology, and controller. This enables the tenants to deploy any network abstraction and application on top of the controller platform, and otherwise it allows a unique shared controller platform to be used for managing multiple domains in a cloud environment. By comparing FlowN and Flowvisor in a scalable virtual networks, they conclude that FlowN has a higher overhead (Latency) due to the database but scales better than FlowVisor (in case of 100 virtual networks or greater).