Recently, handover execution has still been damaging 5G latency requirement due to having three states in virtual Evolved Packet Core (vEPC). Here, the desired End-to-End Delay (e2eDelay) should be less than 4 msecs without any mobility interruption on an enhanced Mobile Broadband (eMBB) service of vEPC. To handle this requirement; we need to focus on the Markov model of e2eDelay. It can be measured by the concatenation of Edge and Core delays in the downlink eMBB service from a remote source to a mobile user. Here, Edge delay is directly affected by the Core network via a decreased packet delivery ratio to Edge under huge-traffic intensity background. Therefore, Target eNodeB decision by considering only Edge network can be misleading. To overcome this, we jointly consider Edge and Core delays which are differently affected by each handover states: (1) preparation, (2) execution, and (3) completion. The joint consideration of Edge and Core can be only handled with a novel cost-effective Software-Defined Ultra-Dense Network (SDUN) framework by dynamically removing state (2). It triggers handover via network-centric monitoring; and then, it predetermines optimal TeNB with a proposed optimization formula and shortest Core path according to traffic intensities of OpenFlow switches. Here; SDUN controller is cost-efficient by the proposed parallel runnable algorithms: Parallel Edge Delay Optimization and Parallel Shortest Delay Path. In the performance evaluation; SDUN is firstly emulated for a specific eMBB traffic, i.e. QUIC based HTTP/3 Video content traffic with 1080p resolution, and secondly simulated in system-level on Matlab. It meets 5G requirements as follows: SDUN decreases Core delay 7.16 msecs per a UE under huge-traffic intensity and keeps Edge delay under 5G requirement with 20% more delivery ratio than conventional one. Moreover, the cost of SDUN controller is analyzed as O( k^4log^2(k) ) and the cost efficiency is observed as the 50% increased scalability level with the acceptable 8% extra virtual memory usage.

In this demo, we provide a Mininet-based Software-Defined Network (SDN) simulation environment that improves the total flow throughput and scalability of the overall network. Mininet is a suitable and manageable tool to implement the proposed SDN based flow admission control module in order to configure the entire topology since it has already built in Open-Flow switches and virtual controllers. This open-source platform is also easily configurable via its drag and drop capabilities. In this Demo, for the Control Plane, OpenDaylight controller is used in order to simulate flow admission control module that fairly admits flows into the OpenFlow switches. OpenFlow version, 1.3. for communication between separated Data and Control plane and Linux based operating system to build Mininet 2.1.0 are deployed in the simulator environment.

In this demo, we implement a realtime testbed for Software Defined Wireless Networks (SDWN) by using Raspberry Pi as OpenFlow (OF) Switches. The implemented testbed provides practical development and testing environment for SDWNs. Moreover, we use OpenDayLight to observe the flows and events in the network. With OpenDayLight integration, we easily provide detailed analysis results for any testing process. Additionally, we develop a traffic aware routing algorithm add-on which manages the flows w.r.t. their QoS requirements. Finally, we physically implement the proposed routing algorithm to our testbed and validate the improvements of traffic aware routing such as controller response time and network utilization.

In smart cities, the business location is an important component of the complex decision process for the entrepreneurs. If a business such as shopping center, restau-rant, coffee shop, hospital, clothing etc. is opened in optimal location; not only the satisfaction level of the customers are enhanced with increased life quality and convenience, but also it maximizes the profit of the entrepreneurs. However, it is complex and long-term decision to find optimal location for a business. Therefore; we propose a smart business application that uses machine learning techniques to estimate the location of a business. The proposed system collects key feature values for a specific business and learns a prediction model for future data. It estimates features and suggests clusters of districts that have optimal locations for that specific business. The proposed system is evaluated on a use case that estimates the best possible locations of a restaurant according to main characteristics taken as inputs from entrepreneurs via a Web based application. These characteristics are Meal Price per customer, HouseHold Type, Gender, and Age. The first phase of decision process is estimating mentioned features such as House Price per district and local population belonging to different HouseHold type, Gender, and Age with less error rates for the consecutive years. Here; we determine an optimal regression model choosing from SMORegression (SMOReg), MultiLayerPerceptron (MLP), and multivariate Linear Regression (Linear) according to Relative Absolute Error (RAE %) parameter, for each feature. The second phase clusters districts according to these estimated features by using hierarchical tree. As a result; the estimated best suggestions for restaurant locations which have similar features, are shown to entrepreneurs via our Web based application. It should be noted that, the application can produce generic solution for the needs of entrepreneurs.

In Software Defined-Content Delivery Networks (SD-CDN), the policies of tenants such as Youtube, Netflix, Office 365, etc. are not the same due to having different 5G traffic requirements for such contents as enhanced Mobile Broadband (eMBB), ultra-reliable low-latency (URLLC) and massive machine-type communication (mMTC). This leads SD-CDN multi-tenant slicing to provide different services with limited network resources, where each tenant can functionally manage their own virtual slice of a physical component according to service level agreements (SLAs). However, they are not permitted to dynamically configure their own components. Therefore, the physical end-to-end configuration of all edge devices causes extra hardware and bandwidth costs. Although software as a service (SaaS) is more preferred to handle cost-efficiency on a switch configuration that increases forwarding throughput (Mbps) with a less number of physical components in SD-CDN, the edge devices can be only served as infrastructure as a service (IaaS) currently. This motivation leads us to serve the switch as a service that includes both IaaS and SaaS characteristics. Therefore, we propose an OpenFlow as a service (OFaaS) design where each tenant has flow management and switch configuration permissions on their own virtual slice. In flow management, we define a novel Service Oriented Architecture (SOA) to orchestrate OFaaS driven topology by isolating each tenant from physical complexity. Here, each tenant can dynamically alter QoS on a flow and load balance between a sub-set of contents via OpenFlow protocol. In switch configuration; a novel OFaaS Management Algorithm for a multi-tenant slicing increases the number of tenants served per OpenFlow switch thanks to OFaaS design. It enables an end-to-end configuration via the NETCONF protocol with a novel YANG model of OFaaS. According to performance evaluation; OFaaS has the same forwarding throughput as conventional IaaS based OpenFlow switch for a homogenous content, whereas it has 71% more forwarding throughput (Mbps) and it has 40% more cost-efficient than conventional one for a heterogeneous content with $17 savings per tenant.