SmartNIC is increasingly utilized to accelerate cloud network components. The effectiveness and correctness of hardware acceleration heavily rely on its management mechanism. Unfortunately, traditional management mechanisms adopt software-centric architecture, which treats flow as the basic management unit and completely relies on one-way commands to manage the flow table, making it challenging to support various cloud network scenarios while managing extremely large tables. In this paper, we advocate for a radical new mechanism to shift the management paradigm from software-centric architecture to hardware-centric architecture, which adopts session as the basic management unit and designs two-way protocols to facilitate the management process. We propose and implement a first-of-its-kind system, called Fornax, a novel management architecture for large public cloud networks. At the core of Fornax is leveraging a session-empowered hardware engine to provide various management capabilities. Besides, Fornax utilizes a light-weight software manager to enhance system scalability, and hardware-driven management protocols to improve resource efficiency. Our testbed evaluations demonstrate that Fornax can reduce the software storage usage by 80% and CPU usage by 77% with little hardware resource overhead. Our large-scale production results show that Fornax can manage up to 16M session entries while significantly reducing the resource overhead by over 79%.

The Digital Twin Network (DTN) is a key enabling technology for efficient and intelligent network management in modern communication networks. Learning dynamic net-work behaviors at the flow granularity is a core element for realizing DTN with accurate network modelling. However, it is challenging due to the complexity of network architectures and the proliferation of emerging network applications. In this paper, we devise a Packet-Action Sequence Model to represent all possible packets behaviors in a unified way. Besides, we propose a novel and effective algorithm to assess whether the behavior pattern is time dependent or independent by using the temporal characteristics of packets in a network flow, so as to learn the key factors of packets that contribute to network behaviors. Based on two typical scenarios, i.e., packet caching and routing, the experimental results verify that the proposed algorithm can identify network behavior patterns and learn key factors affecting the behaviors with over 99 % accuracy.

Due to the ever-increasing complexity of modern communication networks, network operators are making tremendous efforts on achieving objectives for the network to meet the diversified requirements of many real-world applications. However, network operators are repeatedly taking a lot of time on some common tasks shared by different networks. In order to reduce repetitive human efforts on network management, advanced machine learning paradigms, such as deep reinforcement learning, has received numerous attention in the networking community. Nevertheless, it encounters great difficulty in transferring learned policies to new environments, resulting in new model training and testing for each changed environment setting. To tackle this important issue, in this paper we propose a new framework that is the first of its kind to enable an agent to have transferable knowledge for network management, specifically, for network path selection tasks. Through this framework, an agent can efficiently learn and express the transferable network knowledge for achieving task objectives. Extensive experimental results show that the learned knowledge through the proposed framework can realize some common objectives of path selection tasks across different network environments. In addition, the knowledge learned from one network task can significantly improve the learning performance of another similar but different task.