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AKI360: Enabling Highly Interactive 360-degree Video Streaming by Adaptive Keyframe Interval
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Year: 2025 | Conference Paper |
360-degree video is a panoramic video technology designed to offer audience an immersive visual experience. In Motion Constrained Tile Set (MCTS)-based streaming schemes, the server only updates Field-Of-View (FOV) coordinates when codec generating keyframes, thus the keyframe interval significantly impacts FOV updates and interactivity. However, reducing the keyframe interval poses greater challe...Show More
J-Tucker: Joint Compression Scheme for Efficient Deployment of Multi-Task Deep Learning Models on Edge Devices
With the advancement of intelligent edge computing, the deployment of deep learning models (e.g., Convolutional Neural Networks) on edge devices is becoming increasingly popular. However, the limited storage and computing capabilities of these devices present a significant bottleneck for the deployment and execution of large models. In this paper, based on Tucker decomposition, we propose a joint ...Show More
PetTC: Pairwise Joint Embedding Based Contrastive Tensor Completion for Network Traffic Monitoring Services
Haoxuan Wang;Kun Xie;Jigang Wen;Guangxing Zhang;Wei Liang;Gaogang Xie;Kenli Li
Network traffic matrices often suffer from incompleteness and sparsity due to various factors, including network device policies and system limitations. The incompleteness can undermine the reliability and accuracy of network traffic monitoring services, negatively impacting downstream tasks such as network planning and fault diagnosis. Our focus is on network traffic data recovery, intending to i...Show More
Network Monitoring Data Recovery Based on Flexible Bi-Directional Model
Qixue Lin;Xiaocan Li;Kun Xie;Jigang Wen;Shiming He;Gaogang Xie;Xiaopeng Fan;Quan Feng
Comprehensive network monitoring data is crucial for anomaly detection and network optimization tasks. However, due to factors such as sampling strategies and failures in data transmission or storage, only incomplete monitoring data can be obtained. Traditional techniques for completing network monitoring data matrices have limitations in leveraging network-related features and lack the adaptabili...Show More
Enhanced Tube-Based Sampling for Accurate Network Distance Measurement with Minimal Sampling Scheduling Overhead
Jiazheng Tian;Cheng Wang;Kun Xie;Jigang Wen;Gaogang Xie;Kenli Li;Wei Liang
The surge in demand for latency-sensitive services has propelled network distance measurement to the forefront of networking research. Utilizing the low-rank structure of full network data, the tensor completion method can efficiently estimate network distance from partially sampled distance data measured from a small set of node pairs. However, its performance is affected by sampling algorithm li...Show More
Reducing Network Distance Measurement Overhead: A Tensor Completion Solution With a New Minimum Sampling Bound
Jiazheng Tian;Kun Xie;Xin Wang;Jigang Wen;Gaogang Xie;Jiannong Cao;Wei Liang;Kenli Li
Network distance measurement is crucial for evaluating network performance, attracting significant research attention. However, conducting measurements for the entire network is exceedingly expensive and time-consuming, making the reduction of network distance measurement costs a top priority. The tensor completion method efficiently reduces measurement costs by utilizing a small amount of measure...Show More
A Heterogeneous and Adaptive Architecture for Decision-Tree-Based ACL Engine on FPGA
Yao Xin;Chengjun Jia;Wenjun Li;Ori Rottenstreich;Yang Xu;Gaogang Xie;Zhihong Tian;Jun Li
Access Control Lists (ACLs) are crucial for ensuring the security and integrity of modern cloud and carrier networks by regulating access to sensitive information and resources. However, previous software and hardware implementations no longer meet the requirements of modern datacenters. The emergence of FPGA-based SmartNICs presents an opportunity to offload ACL functions from the host CPU, leadi...Show More
Neural Network Compression Based on Tensor Ring Decomposition
Kun Xie;Can Liu;Xin Wang;Xiaocan Li;Gaogang Xie;Jigang Wen;Kenli Li
Deep neural networks (DNNs) have made great breakthroughs and seen applications in many domains. However, the incomparable accuracy of DNNs is achieved with the cost of considerable memory consumption and high computational complexity, which restricts their deployment on conventional desktops and portable devices. To address this issue, low-rank factorization, which decomposes the neural network p...Show More
TCMS: a Multi-sequence Log Parsing Method Based on Token Conversion
Mingkuan Wei;Jigang Wen;Shiming He;Kun Xie;Wei Liang;Gaogang Xie;Kenli Li;Ziyu Zhu
IEEE Transactions on Dependable and Secure Computing
Year: 2024 | Early Access Article |
Detailed system operations are recorded in logs. To ensure system reliability, developers can detect system anomalies through log anomaly detection. Log parsing, which converts semi-structured log messages into structured data, is a crucial step in log anomaly detection and advanced program analysis and verification. Despite the availability of various log parsing tools, they generally suffer from...Show More
SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces
Zhenhe Yao;Haowei Ye;Changhua Pei;Guang Cheng;Guangpei Wang;Zhiwei Liu;Hongwei Chen;Hang Cui;Zeyan Li;Jianhui Li;Gaogang Xie;Dan Pei
2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE)
Year: 2024 | Conference Paper |
Microservice architecture has become a predominant paradigm in the software industry. This architecture necessitates robust end-to-end testing to ensure seamless integration of all components before deployment. Rapidly pinpointing issues when test cases fail is crucial for enhancing software development efficiency. However, in testing environments, the available trace is often sparse, and the syst...Show More
LogGenius: An Unsupervised Log Parsing Framework with Zero-shot Prompt Engineering
Xian Yu;Shengxi Nong;Dongbiao He;Weijie Zheng;Teng Ma;Ning Liu;Jianhui Li;Gaogang Xie
2024 IEEE International Conference on Web Services (ICWS)
Year: 2024 | Conference Paper |
Efficient and accurate parsing of unstructured logs is crucial for anomaly detection, root cause localization, and log compression. Although many existing works have made good progress relying on Large Language Models (LLMs) and prompt engineering techniques, most of them require a certain degree of labeling or few-shot prompts, which limits their applicability in large-scale real-time heterogeneo...Show More
GraphIoT: Lightweight IoT Device Detection Based on Graph Classifiers and Incremental Learning
Yansong Yin;Kun Xie;Shiming He;Yanbiao Li;Jigang Wen;Zulong Diao;Dafang Zhang;Gaogang Xie
The rapid expansion of the Internet of Things (IoT) has led to growing concerns about the security of IoT devices. A crucial aspect of ensuring their security is IoT device identification, which involves pinpointing the specific type of device. Existing solutions, however, either necessitate complex feature engineering or struggle to handle the ever-increasing number of new devices in open IoT env...Show More
Tensor Factorization for Accurate Anomaly Detection in Dynamic Networks
IEEE Transactions on Sustainable Computing
Year: 2024 | Early Access Article |
Accurately detecting traffic anomalies becomes increasingly crucial in network management. Algorithms that model the traffic data as a matrix suffers from low detection accuracy, while the work using the tensor model often assumes the tensor is regular without considering that network nodes may dynamically join in or leave, which will fail in a practical network with the change of node set as a re...Show More
GDI: A Novel IoT Device Identification Framework via Graph Neural Network-Based Tensor Completion
Haoxuan Wang;Kun Xie;Xin Wang;Jigang Wen;Ruotian Xie;Zulong Diao;Wei Liang;Gaogang Xie;Jiannong Cao
Accurately identifying IoT device types is crucial for IoT security and resource management. However, existing traffic-based device identification algorithms incur high measurement, storage, and computation costs, as they continuously need to capture, store, and parse device traffic. To overcome these challenges, we propose an innovative framework that employs a discontinuous traffic measurement s...Show More
MLPing: Real-Time Proactive Fault Detection and Alarm for Large-Scale Distributed IDC Network
Xian Yu;Kejiang Ye;Dongbiao He;Xiaofan Chen;Chengzhong Xu;Jianhui Li;Gaogang Xie
2024 IEEE 44th International Conference on Distributed Computing Systems (ICDCS)
Year: 2024 | Conference Paper |
Through providing cheap rack and network hosting services, third-party internet data centers (IDCs) have gained significant popularity among cloud service providers. Real-time monitoring of the quality of the IDC network and proactively alarming is crucial to guaranteeing the reliability of cloud services. The prevailing approach to addressing this problem involves utilizing active probes and maki...Show More
An Accuracy-Preserving Neural Network Compression via Tucker Decomposition
IEEE Transactions on Sustainable Computing
Year: 2024 | Early Access Article |
Deep learning has made remarkable progress across many domains, enabled by the capabilities of over-parameterized neural networks with increasing complexity. However, practical applications often necessitate compact and efficient networks because of device constraints. Among recent low-rank decomposition-based neural network compression techniques, Tucker decomposition has emerged as a promising m...Show More
M$^{2}$STL: Multi-Range Multi-Level Spatial-Temporal Learning Model for Network Traffic Prediction
Ruotian Xie;Jigang Wen;Xiaodi Chen;Kun Xie;Wei liang;Naixue Xiong;Dafang Zhang;Gaogang Xie;Kenli Li
Network traffic prediction is crucial for network management, resource allocation, congestion control, and network anomaly detection. However, existing studies on network traffic prediction often fail to fully utilize the complex spatial-temporal dependencies present in network traffic data. This paper introduces a deep learning-based network traffic prediction model, named Multi-range Multi-level...Show More
Per-Packet Traffic Measurement in Storage, Computation and Bandwidth Limited Data Plane
Yinchuan Cong;Kun Xie;Jigang Wen;Jiwei Zhang;Yansong Yin;Qingyi Zhang;Gaogang Xie;Wei Liang
Packet level measurement in the data plane provides a microscopic view of the network’s state. Although advances in programmable switches and routers make it possible to measure the Sequence of Packet Lengths and Arrival Times (SPLT) in the data plane, collecting this information remains challenging due to limited storage, processing resources, and bandwidth. To address this issue, we propose MES,...Show More
A Light-Weight and Robust Tensor Convolutional Autoencoder for Anomaly Detection
Xiaocan Li;Kun Xie;Xin Wang;Gaogang Xie;Kenli Li;Jiannong Cao;Dafang Zhang;Jigang Wen
Robust PCA is a popular anomaly detection technique and has been widely used in many applications. Although Robust PCA is promising, it is usually designed in a two-order matrix form, which is inferior to the tensor that can capture multilinearity features of data. Moreover, the detection accuracy under Robust PCA further suffers due to its sensitivity to the rank parameter which is hard to set in...Show More
DMSTG: Dynamic Multiview Spatio-Temporal Networks for Traffic Forecasting
Zulong Diao;Xin Wang;Dafang Zhang;Gaogang Xie;Jianguo Chen;Changhua Pei;Xuying Meng;Kun Xie;Guangxing Zhang
Traffic sensor networks are widely applied in smart cities to monitor traffic in real-time. Exploiting such data to forecast future traffic conditions has the potential to enhance the decision-making capabilities of intelligent transportation systems, which attracts widespread attention from both industries and academia. Among them, network-wide prediction based on graph convolutional neural netwo...Show More
Accurate Throughput Prediction for Improving QoE in Mobile Adaptive Streaming
Gerui Lv;Qinghua Wu;Qingyue Tan;Weiran Wang;Zhenyu Li;Gaogang Xie
Video streaming is the most important mobile application today. To improve users’ quality of experience (QoE), the client player runs adaptive bitrate (ABR) algorithms that dynamically select the bitrate for video chunks based on throughput or delivery time predictions. This paper aims to design an accurate predictor for mobile adaptive streaming by investigating all its components, including inpu...Show More
CS-Sketch: Compressive Sensing Enhanced Sketch for Full Traffic Measurement
Traffic measurement is crucial for lots of network applications such as network management, anomaly detection, and traffic engineering. Although existing Sketch-based algorithms have made lots of efforts for traffic measurement, they can only perform accurate measurements of the elephant flows while badly on the mice flows. Inspired by Compressive Sensing(CS), we design a lightweight traffic measu...Show More
BurstBalancer: Do Less, Better Balance for Large-Scale Data Center Traffic
Zirui Liu;Yikai Zhao;Zhuochen Fan;Tong Yang;Xiaodong Li;Ruwen Zhang;Kaicheng Yang;Zihan Jiang;Zheng Zhong;Yi Huang;Cong Liu;Jing Hu;Gaogang Xie;Bin Cui
Layer-3 load balancing is a key topic in the networking field. It is well acknowledged that flowlet is the most promising solution because of its good trade-off between load balance and packet reordering. However, we find its one significant limitation: it makes the forwarding paths of flows unpredictable. To address this limitation, this article presents BurstBalancer, a simple yet efficient load...Show More
HPETC: History Priority Enhanced Tensor Completion for Network Distance Measurement
Cheng Wang;Kun Xie;Jiazheng Tian;Jigang Wen;Xiaocan Li;Gaogang Xie;Kenli Li
In network distance measurement, how to estimate the whole network distance data from partially observed samples has attracted lots of attention because of its significance for network performance evaluation. Matrix completion becomes the most effective approach. However, the two-dimension matrix can only capture the spatial features in the network distance data while ignoring the temporal feature...Show More
MALT: Fine-Grained Microservice Profiling for Request Latency Anomaly Localization
Dingjie Zhang;Qinghua Wu;Yuankang Zhao;Zhenyu Li;Peng He;Yongsu Zhang;Xiongchun Duan;Yu Zhang;Gen Miao;Jiancong Tong;Gaogang Xie
Recent years have witnessed the shift of cloud datacenter architecture from monolithic to microservice. In microservice, request latency which fundamentally determines application performance, has become a crucial concern for cloud operators. Due to various issues with hosts, networks, and remote services, microservices can encounter anomalies when processing requests, causing sudden latency spike...Show More
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