## 4 Implementation
### Flow-Centric Visibility to Assist Networking Control
## 5 Experimental Results

We conducted experimental evaluations over the OF@TEIN Playground [26] where we verified the end-to-end integration of proposed solutions, demonstrating how packet tracing results were analyzed at the visibility center and translated into ONOS intents for networking control. Performance measurements presented in this section include assessing the efficiency of IO Visor-based packet tracing, the clustering efficiency of our flow-centric visibility solution, and the execution time of our ONOS intent-based networking application, showcasing the scalability and effectiveness of our proposals. ### Experimental Environment

To verify proposed solutions for enhancing networking control through intent-driven mechanisms and flow-centric visibility, we conducted experimental simulations using the OF@TEIN Playground. OF@TEIN is a distributed multi-site cloud testbed enabled with SDN technology, linking 10 international sites across nine Asian countries. It dynamically allocates resources for a variety of experiments. The playground operates with Playground Tower overseeing its functions. The topology of our experimental environment is depicted in Figure 7. During the experiment, we collected network traffic data from the playground sites, where Linux servers (referred to as SmartX boxes) are hosted. Subsequently, these traffic data were analyzed at the visibility center to formulate recommended policies for each identified flow class, as per our flow-centric visibility solution. Figure 6: ONOS intent-leveraged application logic. ### End-to-End Integration Verification

To verify the end-to-end integration of the three proposed solutions, we first ran packet tracing on specific physical network interfaces of the SmartX box, where all the packets are coming/going from/to the box/site. Figure 8 shows the result of packet tracing, which contains traced network packet data and is sent to the visibility center for analysis. This figure also shows the analysis results of the collected data, organized based on IP prefix and their assigned recommended policies. Following the analysis, the ONOS application identified the authorized AS number and SDN controller information. Subsequently, it translated the recommended policies into ONOS intents, as depicted in the figure. ### Performance Measurements

#### 5.3.1 (Kernel/User)-Space Tracing Performance of IO Visor-Based Packet Tracing

To show the efficiency of IO Visor-based packet tracing collection, we measured the Linux-based SmartX box performance during the data collection for both base collection and TCP-sync collection. We also performed measurements in two different spaces of the

Figure 8: The overall verification of our proposed solution that consists of several integration works. Figure 7: Experimental simulation over OF@TEIN Playground. OS for tracing collection. First is in the kernel space using BPF bytecode. Second is in the user space (using the Python program with a BCC library) where a string is converted into byte arrays, which is very similar to existing packet-capturing tools. We assessed the CPU utilization, memory usage, and amount of collected packet data under various traffic patterns, including background and attack traffic, generated within our testing environment. Iperf [27] was utilized to generate background UDP traffic with a specific size (i.e., 10 MB). The Nmap scanning tool [28] was used to randomly generate TCP-sync traffic at different rates, resulting in varying amounts of attack traffic (i.e., 1, 2, 5, and 10 MB). Figure 9 shows the measurement results of the performed experiments. The CPU performance of base collection is almost similar for all traffic patterns, in either the user space or kernel space. However, the TCP-sync collection has significantly different values, which are around 10%. This indicates that implementing IO Visor-based packet tracing in the kernel space gives better performance than the tracing in the user space.