## 4 Implementation
### Flow-Centric Visibility to Assist Networking Control
Some may send malicious or attack traffic, so the IXP provider should deploy visibility solutions at this point of contact and implement control mechanisms to block any attack traffic. While this paper takes a significant step towards addressing the challenges of managing networking controls in multi-site cloud environments, there are a few areas we identified for further research and development to facilitate testing and deployment in production infrastructures. Key enhancements should focus on the scalability and performance aspects of this work, including:

* _Utilizing AI_ to speed up the decision-making process based on the vast amount of collected network packet flow data. More precisely, deploying specific models to detect attack traffic tailored to particular infrastructure targets is becoming increasingly feasible with current emerging algorithms and tools. For instance, using AI algorithms, one can more efficiently and precisely recognize and categorize traffic patterns, distinguishing between normal and suspicious flows. * _Hardware acceleration_ to deploy policy in the networking hardware (i.e., networking chip). Deploying policies directly onto the hardware will enable the system to handle larger scales of network data/information, such as Internet routes with hundreds of thousands of prefixes. * _Extending the applicability_ of our solution to support emerging technologies and architectures, such as 5G and edge computing, presents an exciting future research direction. These environments have unique requirements and characteristics that our solution could address with some adaptations and enhancements. Figure 12: Measurement results of ONOS intent installation time. ## 7 Conclusions

This paper presents an integrated solution for addressing the challenges posed by the changing landscape of multi-site cloud infrastructures interconnected via L3 IP networks. We have designed a comprehensive solution that combines IO Visor-enabled network packet tracing for efficient flow-level visibility, IP prefix-based flow analysis for intent-based networking control, and the utilization of the ONOS SDN controller for managing hybrid SDN-IP interconnections. Our approach enables the seamless integration of monitoring and control mechanisms, which improves the overall adaptability and security of interconnected cloud sites. By leveraging flow-level visibility, our solution enables the timely measurement and analysis of network packet flows across multiple servers and physical locations. This capability is essential for ensuring the smooth operation and security of multi-site cloud environments. Additionally, our approach facilitates intent-based networking control, allowing for the implementation of diverse policies and configurations across heterogeneous network domains. In particular, it can improve the performance of hybrid SDN-IP interconnection in a multi-site SDN-enabled environment under different network administrators and control domains by reducing unnecessary flow across the interconnection. We evaluated our solution in the OF@TEIN Playground, a distributed testbed environment that provides multi-site cloud infrastructures. Our experimental results showed the effectiveness of our solution, where we analyzed network traffic, classified flows, and dynamically managed network policies through the ONOS SDN controller. Overall, performance evaluations showed that IO Visor-based packet tracing in the kernel space outperforms user-space tracing in terms of CPU utilization. Additionally, our Spark-based visibility integration processed a large number of packet flows efficiently, with processing times scaling linearly with the data size. The intent-based application on the ONOS controller demonstrated rapid policy installation times, significantly faster than BGP routing convergence times, even with large numbers of intents. This work is conducted together by [PERSON], [PERSON] and [PERSON]. [PERSON], [PERSON] and [PERSON] designed and developed the proposed system in this work. [PERSON] evaluated proposed system.