# Dynamic Virtual Network Slicing and Orchestration for Selective MEC Services over Wide-Area SDN
## 2 KREONET-S: Wide-Area SDN Infrastructure Development and Deployment
As a control plane for KREONET-S, an open-source SDN control platform, Open Network Operating System (ONOS) [24] developed by ONF is deployed on top of the infrastructure layer with several key application modules newly developed (e.g., reactive forwarding module, topology synchronization module, configuration and recovery module) in order to improve the overall operational stability in the control layer of KREONET-S. The core architecture of ONOS enables individual controller instances to be clustered as one logically centralized controller using eventual and strong consistency in distributed datastores, providing high availability, scalability, and centralized management. The distributed datastores synchronize the states of each controller instance on the basis of appropriate state distribution and consensus mechanisms such as the Atomix framework, Raft algorithm, and gossip protocol. By adopting the ONOS distributed core architecture, KREONET-S operates a three-node ONOS cluster utilizing OpenFlow as the SBI to collect network status and topology information from the widely dispersed network devices in eight locations, and accordingly creates a global network topology as indicated in Figure 3. The network topology, status, and event information is abstracted by ONOS and delivered to the virtualization layer (also known as the VDN) of KREONET-S through the NBI so that the VDN can provide and manage virtual network slices on the basis of the abstracted global network topology and state information. VDN basically allocates dedicated network slices so that UEs and intelligent wireless/wired devices can access MEC nodes with optimized latency and bandwidth on the basis of its dynamic and on-demand network resource provisioning capability. Isolating the network slices using SDN in this paper is achieved by the flow isolation mechanism on the basis of the OpenFlow protocol, which is combined with the technologies of data delivery path calculation, VDN reactive forwarding (vFwd), virtual dynamic host configuration protocol (vDHCP), virtual network access control (vNAC), and VDN federation (vFED), as shown in Figure 2. Each generated slice is labeled with a specific VDN identifier, and then strictly isolated and separated from the other slices on the basis of a segregated flow-rule installation to the corresponding network devices so that end-hosts and UEs in the same slice can only communicate with each other. The VDN mechanism is described in detail in Section 3. Moreover, when computing and storage resources become insufficient by rapidly increasing the number of user devices (e.g., smartphones and smart cars), VDN can provide (location-aware) proximity services for the latency-sensitive and computation-intensive user devices by calculating the shortest path that secures the required bandwidth from user devices to a selective MEC site in the nearest on-site access point or edge network. For example, considering a real-time adaptive video streaming service, a number of dynamically increasing user devices need to receive the stream seamlessly from the first-zone MEC site (on-site access point) or the nearest next-zone MEC site on the basis of the selective MEC service model. For this type of service scenario, we consider the hierarchical and multiple-zone MEC service architecture depicted in Figure 3, and the architecture is integrated with the underlying KREONET-S global network topology, as shown in Figure 4.  
The hierarchical MEC architecture was introduced in several studies such as radio-aware video optimization in a fully virtualized network (RAVEN) experiment activity [25] and hierarchical MEC server deployment research [26], where RAVEN embraces an SD-RAN (Software Defined Radio Access Network) controller to facilitate communication between the controller and the agent residing on network devices, and the latter proposes an integer nonlinear programming model to minimize the resource deployment cost and average latency. Similarly, the MEC service architecture of KREONET-S incorporates a hierarchical model.