Traffic classification has a significant impact on maintaining the Quality of Service (QoS) of the network. Since traditional methods heavily rely on feature extraction and largescale labeled data, some recent pre-trained models manage to reduce the dependency by utilizing different pre-training tasks to train generic representations for network packets. However, existing pre-trained models typically adopt pre-training tasks developed for image or text data, which are not tailored to traffic data. As a result, the obtained traffic representations fail to fully reflect the information contained in the traffic, and may even disrupt the protocol information. To address this, we propose TraGe, a novel generic packet representation model for traffic classification. Based on the differences between the header and payload-the two fundamental components of a network packetwe perform differentiated pre-training according to the byte sequence variations (continuous in the header vs. discontinuous in the payload). A dynamic masking strategy is further introduced to prevent overfitting to fixed byte positions. Once the generic packet representation is obtained, TraGe can be finetuned for diverse traffic classification tasks using limited labeled data. Experimental results demonstrate that TraGe significantly outperforms state-of-the-art methods on two traffic classification tasks, with up to a 6.97% performance improvement. Moreover, TraGe exhibits superior robustness under parameter fluctuations and variations in sampling configurations.

Encryption is crucial for securing critical network infrastructures, including datacenter networks, 5 G networks, and the Internet of Things (IoT). In-network encryption (INE) offers a promising solution by enabling direct encryption of data on the network's data plane during transmission, thereby eliminating the need for host-side hardware encryption. However, existing INE solutions fail to fully leverage the processing capabilities of programmable switches, leading to low throughput, high resource overhead, and limited key flexibility. These limitations hinder their compatibility with other network functions and restrict their real-world deployment. To address these challenges, we introduce NET-SM4, a high-performance secure encryption mechanism based on in-network computing. NET-SM4 offloads the highly secure and pipeline-optimized SM4 encryption algorithm to programmable switches. By employing a hardwarefriendly table lookup approach, NET-SM4 reduces computation dependency chains and supports parallel encryption inherently, thereby achieving high throughput and low resource overhead for in-network encryption. We implement a prototype of NETSM4 on a commercial Tofino switch and evaluate its performance through testbed experiments and a real-world RDMA-based case study. The results demonstrate that NET-SM4 (1) outperforms state-of-the-art in-network encryption solutions in throughput by up to 293.85 %, and (2) ensures link-speed data transmission with less than$20 \mu$s overhead in real-world scenarios.

In this paper, we study an online cost optimization problem for distributed data access. The goal of this problem is to dynamically create and delete data copies in a multi-server distributed system as time goes, in order to minimize the total storage and network cost of serving access requests. We propose an online algorithm with randomized storage periods of data copies in the servers, and derive an optimal probability density function of storage periods, which makes the algorithm achieve a competitive ratio of $1 + \frac{{\sqrt 2 }}{2}$. An example is presented to show that the competitive analysis of our algorithm is tight. Experimental evaluations using real data access traces demonstrate that our algorithm outperforms the best known deterministic algorithm.

As the increase of geographically distributed applications continues, the demand for high-speed, long-distance data transmission across wide area networks (WANs) has significantly increased. Remote Direct Memory Access (RDMA) is extensively deployed in data center networks (DCNs) for its high throughput, low latency, and reduced CPU utilization, and its extension to WANs is expected to fully leverage these benefits. However, existing RDMA solutions, while demonstrating superior performance in data centers, face a performance gap over WANs due to their reliance on DCNs for optimal performance and lack of optimization for WANs’ high latency and loss rates. To bridge this gap, we introduce Lossy Wide-Area RDMA (LoWAR), a high-goodput, high-reliability RDMA solution for lossy WANs. LoWAR incorporates a forward error correction (FEC) shim layer to protect RDMA messages from packet loss, thus minimizing the inefficiency of retransmissions. It also fully offloads processing to RNICs with minimal computational overhead and storage burden, operating transparently on RNICs without requiring modifications to existing applications and networks. We implement a LoWAR prototype with FPGA and evaluate its performance through testbed experiments. The results demonstrate LoWAR’s enhanced performance in lossy WANs: in WANs with 40ms RTT and 0.001% to 0.01% loss rates, LoWAR increases RDMA goodput by 2.05 to 5.01 times, reduces average flow completion times (FCTs) by 3.5% to 12.2%, and eliminates 99th percentile tail FCTs in most scenarios.

This paper studies an online replication problem for distributed data access. The goal is to dynamically create and delete data copies in a multi-server system as time passes to minimize the total storage and network cost of serving access requests. We study the problem in the emergent learning-augmented setting, assuming simple binary predictions about inter-request times at individual servers. We develop an online algorithm and prove that it is (5+α/3)-consistent (competitiveness under perfect predictions) and (1+1/α)-robust (competitiveness under terrible predictions), where α◰(0, 1] is a hyper-parameter representing the level of distrust in the predictions. We also study the impact of mispredictions on the competitive ratio of the proposed algorithm and adapt it to achieve a bounded robustness while retaining its consistency. We further establish a lower bound of 3/2 on the consistency of any deterministic learning-augmented algorithm. Experimental evaluations are carried out to evaluate our algorithms using real data access traces.

Whole genome sequencing (WGS) can provide insight into drug-resistance, transmission chains and the identification of outbreaks, but data analysis remains an obstacle to its routine clinical use. Although several drug-resistance prediction tools have appeared, until now no website integrates drug-resistance prediction with strain genetic relationships and species identification of nontuberculous mycobacteria (NTM). We have established a free, function-rich, user-friendly online platform for MTB WGS data analysis (SAM-TB, http://samtb.szmbzx.com) that integrates drug-resistance prediction for 17 antituberculosis drugs, detection of variants, analysis of genetic relationships and NTM species identification. The accuracy of SAM-TB in predicting drug-resistance was assessed using 3177 sequenced clinical isolates with results of phenotypic drug-susceptibility tests (pDST). Compared to pDST, the sensitivity of SAM-TB for detecting multidrug-resistant tuberculosis was 93.9% [95% confidence interval (CI) 92.6-95.1%] with specificity of 96.2% (95% CI 95.2-97.1%). SAM-TB also analyzes the genetic relationships between multiple strains by reconstructing phylogenetic trees and calculating pairwise single nucleotide polymorphism (SNP) distances to identify genomic clusters. The incorporated mlstverse software identifies NTM species with an accuracy of 98.2% and Kraken2 software can detect mixed MTB and NTM samples. SAM-TB also has the capacity to share both sequence data and analysis between users. SAM-TB is a multifunctional integrated website that uses WGS raw data to accurately predict antituberculosis drug-resistance profiles, analyze genetic relationships between multiple strains and identify NTM species and mixed samples containing both NTM and MTB. SAM-TB is a useful tool for guiding both treatment and epidemiological investigation.

Pedestrian Protection System (PPS) has become an active research area aimed to protect pedestrians by assisting inattentive drivers. Despite the enormous number of research works, most current systems are designed to recognize near-scale pedestrians. However, they perform poorly in mid-scale and fail in far-scale. In this paper, a new hardware-based architecture that detects pedestrians in near-, mid- and far-scales is introduced. Two regions are defined in this architecture: the near-to-mid area located in front of vehicles, called Region of High Risk (RHR); and the mid-to-far area, called Region of Low Risk (RLR). To implement this system, we use two identical cameras, each of which is equipped with a variable focal length lens. Moreover, we develop a mathematical model for our framework. Finally, we conduct a set of experiments in open park areas and in Ottawa roads. The results show that the system is able to accurately detect pedestrians that are located up to 130 meters away from the vehicle.