Given the high packet processing efficiency of programmable switches (e.g., P4 switches of Tbps), several works are proposed to offload the decision tree (DT) to P4 switches for in-network classification. Although the DT is suitable for the match-action paradigm in P4 switches, the range match rules used in the DT may not be supported across devices of different P4 standards. Additionally, emerging models including neural networks (NNs) and ensemble models, have shown their superior performance in networking tasks. But their sophisticated operations pose new challenges to the deployment of these models in switches. In this paper, we propose Mousikav2 to address these drawbacks successfully. First, we design a new tree model, i.e., the binary decision tree (BDT). Unlike the DT, our BDT consists of classification rules in the form of bits, which is a good fit for the standard ternary match supported by different hardware/software switches. Second, we introduce a teacher-student knowledge distillation architecture in Mousikav2, which enables the general transfer from other sophisticated models to the BDT. Through this transfer, sophisticated models are indirectly deployed in switches to avoid switch constraints. Finally, a lightweight P4 program is developed to perform classification tasks in switches with the BDT after knowledge distillation. Experiments on three networking tasks and three commodity switches show that Mousikav2 not only improves the classification accuracy by 3.27%, but also reduces the switch stage and memory usage by$2.00\times $and 28.67%, respectively. Code is available athttps://github.com/xgr19/Mousika.

Given the power efficiency and Tbps throughput of packet processing, several works are proposed to offload the decision tree (DT) to programmable switches, i.e., in-network intelligence. Though the DT is suitable for the switches’ match-action paradigm, it has several limitations. E.g., its range match rules may not be supported well due to the hardware diversity; and its implementation also consumes lots of switch resources (e.g., stages and memory). Moreover, as learning algorithms (particularly deep learning) have shown their superior performance, some more complicated learning models are emerging for networking. However, their high computational complexity and large storage requirement are cause challenges in the deployment on switches. Therefore, we propose Mousika, an in-network intelligence framework that addresses these drawbacks successfully. First, we modify the DT to the Binary Decision Tree (BDT). Compared with the DT, our BDT supports faster training, generates fewer rules, and satisfies switch constraints better. Second, we introduce the teacher-student knowledge distillation in Mousika, which enables the general translation from other learning models to the BDT. Through the translation, we can not only utilize the super learning capabilities of complicated models, but also avoid the computation/memory constraints when deploying them on switches directly for line-speed processing.

Nowadays, the surge of Internet contents and the need for high Quality of Experience (QoE) put the backbone network under unprecedented pressure. The emerging edge caching solutions help ease the pressure by caching contents closer to users. However, these solutions suffer from two challenges: 1) a low hit ratio due to edges’ high density and small coverages. 2) unbalanced edges’ workloads caused by dynamic requests and heterogeneous edge capacities. In this paper, we formulate a typical cooperative edge caching problem and propose the MagNet, a decentralized and cooperative edge caching system to address these two challenges. The proposed MagNet system consists of two innovative mechanisms: 1) the Automatic Content Congregating (ACC), which utilizes a neural embedding algorithm to capture underlying patterns of historical traces to cluster contents into some types. The ACC then can guide requests to their optimal edges according to their types so that contents congregate automatically in different edges by type. This process forms a virtuous cycle between edges and requests, driving a high hit ratio. 2) the Mutual Assistance Group (MAG), which lets idle edges share overloaded edges’ workloads by forming temporary groups promptly. To evaluate the performance of MagNet, we conduct experiments to compare it with classical, Machine Learning (ML)-based and cooperative caching solutions using the real-world trace. The results show that the MagNet can improve the hit ratio from 40% and 60% to 75% for non-cooperative and cooperative solutions, respectively, and significantly improve the balance of edges’ workloads.

The Border Gateway Protocol (BGP) is arguably the most important and irreplaceable protocol in the network. However, the lack of routing authentication and validation makes it vulnerable to attacks, including routing leaks, route hijacking, prefix hijacking, etc. Therefore, in this paper we propose a generalized framework for ISP self-operated BGP anomaly detection based on weakly supervised learning. To tackle the problem of insufficient data in BGP anomaly detection, we propose an approach to learn from the other anomaly detection systems through knowledge distillation. To reduce the impact of inaccurate supervision, we design a self-attention-based Long Short-Term Memory (LSTM) model to self-adaptively mine the differences between BGP anomaly categories, including both feature and time dimensions. Finally, we implement a system and demonstrate the performance through a set of comprehensive experiments. Compared with the state-of-the-art schemes, our scheme has better generalization on various anomaly types.

Rate control plays an important role in video coding. It regulates the coded bits to satisfy the channel rate while keeping good video quality. In Tsai's paper, a new algorithm was proposed which rearranging the macroblocks' coding order according to their significance in each frame. More complex macroblocks will be coded with more priority in coding order. But it is required to rearrange the coding order and output the encoded bits in the original scan order. In this paper, a new rate control algorithm is proposed which recalculates the mquant of each macroblock inside a frame according to its significance. Furthermore, it can be applied to all macroblock-level coders. Simulation results show that this new algorithm can achieve obvious improvement in video quality like Tsai's algorithm while has lower complexity

In motion-compensated video coding, the errors caused by packet loss not only impair the reconstruction quality of current frame, but also lead to error propagation to subsequent frames. Based on the error-propagation analysis in a group of pictures (GOP), we propose an unequal forced intra-refresh scheme to increase error resilience of multicast video. According to a GOP-level error-propagation model, the proposed scheme can distribute the unequal number of forced intra-mode MBs to different P-frames of a GOP. Experimental results show that the proposed scheme can effectively mitigate the error-propagation effect and achieve about 0.1~1.1 dB gains over the traditional average scheme in H.264/AVC