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Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services

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arXiv 2023年

作者： Liu, Jinyang Yang, Tianyi Chen, Zhuangbin Su, Yuxin Feng, Cong Yang, Zengyin Lyu, Michael R. The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-sen University Zhuhai China Computing and Networking Innovation Lab Huawei Cloud Computing Technology Co. Ltd China

As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems’ health status, becomes more and more critical and challenging. In particular, the dependency between different metrics and their historical patterns plays a critical role in pursuing prompt and accurate anomaly detection. Existing approaches fall short of industrial needs for being unable to capture such information efficiently. To fill this significant gap, in this paper, we propose CMAnomaly, an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed collaborative machine is a mechanism to capture the pairwise interactions along with feature and temporal dimensions with linear time complexity. Cost-effective models can then be employed to leverage both the dependency between monitoring metrics and their historical patterns for anomaly detection. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of huawei Cloud. The experimental results demonstrate that compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% ∼ 10.68%, and runs 10× ∼ 20× faster. Furthermore, we also share our experience of deploying CMAnomaly in huawei Cloud. © 2023, CC BY-NC-ND.

关键词： software reliability

Structure-Preserving Graph Representation Learning

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Structure-Preserving Graph Representation Learning

IEEE International Conference on Data Mining (ICDM)

作者： Ruiyi Fang Liangjian Wen Zhao Kang Jianzhuang Liu School of Information and Software Engineering University of Electronic Science and Technology of China The Noah’s Ark Lab Huawei Technologies Company Limited School of Computer Science and Engineering University of Electronic Science and Technology of China

Though graph representation learning (GRL) has made significant progress, it is still a challenge to extract and embed the rich topological structure and feature information in an adequate way. Most existing methods focus on local structure and fail to fully incorporate the global topological structure. To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs. Specifically, to reduce the uncertainty and misinformation of the original graph, we construct a feature graph as a complementary view via k-Nearest Neighbor method. The feature graph can be used to contrast at node-level to capture the local relation. Besides, we retain the global topological structure information by maximizing the mutual information (MI) of the whole graph and feature embeddings, which is theoretically reduced to exchanging the feature embeddings of the feature and the original graphs to reconstruct themselves. Extensive experiments show that our method has quite superior performance on semi-supervised node classification task and excellent robustness under noise perturbation on graph structure or node features. The source code is available at https://***/uestc-lese/SPGRL.

关键词： Representation learning Uncertainty Source coding Perturbation methods Feature extraction Robustness Data mining

Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems

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arXiv 2023年

作者： Liu, Jinyang Jiang, Zhihan Gu, Jiazhen Huang, Junjie Chen, Zhuangbin Feng, Cong Yang, Zengyin Yang, Yongqiang Lyu, Michael R. The Chinese University of Hong Kong Hong Kong School of Software Engineering Sun Yat-Sen University Zhuhai China Computing and Networking Innovation Lab. Huawei Cloud Computing Technology Co. Ltd. China

Ensuring the reliability of cloud systems is critical for both cloud vendors and customers. Cloud systems often rely on virtualization techniques to create instances of hardware resources, such as virtual machines. However, virtualization hinders the observability of cloud systems, making it challenging to diagnose platform-level issues. To improve system observability, we propose to infer functional clusters of instances, i.e., groups of instances having similar functionalities. We first conduct a pilot study on a large-scale cloud system, i.e., huawei Cloud, demonstrating that instances having similar functionalities share similar communication and resource usage patterns. Motivated by these findings, we formulate the identification of functional clusters as a clustering problem and propose a non-intrusive solution called Prism. Prism adopts a coarse-to-fine clustering strategy. It first partitions instances into coarse-grained chunks based on communication patterns. Within each chunk, Prism further groups instances with similar resource usage patterns to produce fine-grained functional clusters. Such a design reduces noises in the data and allows Prism to process massive instances efficiently. We evaluate Prism on two datasets collected from the real-world production environment of huawei Cloud. Our experiments show that Prism achieves a v-measure of ∼0.95, surpassing existing state-of-the-art solutions. Additionally, we illustrate the integration of Prism within monitoring systems for enhanced cloud reliability through two real-world use cases. © 2023, CC BY-NC-ND.

关键词： Observability

Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems

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Prism: Revealing Hidden Functional Clusters from Massive Ins...

IEEE International Conference on Automated software engineering (ASE)

作者： Jinyang Liu Zhihan Jiang Jiazhen Gu Junjie Huang Zhuangbin Chen Cong Feng Zengyin Yang Yongqiang Yang Michael R. Lyu The Chinese University of Hong Kong Hong Kong SAR China School of Software Engineering Sun Yat-sen University Zhuhai China Computing and Networking Innovation Lab Huawei Cloud Computing Technology Co. Ltd China

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Code Structure Guided Transformer for Source Code Summarization

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arXiv 2021年

作者： Gao, Shuzheng Gao, Cuiyun He, Yulan Zeng, Jichuan Nie, Lun Yiu Xia, Xin Lyu, Michael R. Harbin Institute of Technology Shenzhen China University of Warwick United Kingdom The Chinese University of Hong Kong Hong Kong Tsinghua University China Software Engineering Application Technology Lab Huawei China

Code summaries help developers comprehend programs and reduce their time to infer the program functionalities during software maintenance. Recent efforts resort to deep learning techniques such as sequence-to-sequence models for generating accurate code summaries, among which Transformer-based approaches have achieved promising performance. However, effectively integrating the code structure information into the Transformer is under-explored in this task domain. In this paper, we propose a novel approach named SG-Trans to incorporate code structural properties into Transformer. Specifically, we inject the local symbolic information (e.g., code tokens and statements) and global syntactic structure (e.g., data flow graph) into the self-attention module of Transformer as inductive bias. To further capture the hierarchical characteristics of code, the local information and global structure are designed to distribute in the attention heads of lower layers and high layers of Transformer. Extensive evaluation shows the superior performance of SG-Trans over the state-of-the-art approaches. Compared with the best-performing baseline, SG-Trans still improves 1.4% and 2.0% in terms of METEOR score, a metric widely used for measuring generation quality, respectively on two benchmark datasets. Copyright © 2021, The Authors. All rights reserved.

关键词： Graphic methods

Pyscribe - Learning to Describe Python Code

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SSRN

SSRN 2022年

作者： Guo, Juncai Liu, Jin Wan, Yao Zhao, Yanjie Li, Li Liu, Kui Klein, Jacques Bissyandé, Tegawendé F. School of Computer Science Wuhan University Wuhan China Key Laboratory of Network Assessment Technology Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Faculty of Information Technology Monash University VIC Australia Huawei Software Engineering Application Technology Lab Hangzhou China SnT Centre University of Luxembourg 1855 Luxembourg

Code comment generation, which aims to describe the functionality of source code in natural language, plays an important role in automatic software development. Currently, several structural neural networks have been exploited to preserve the syntax structure of source code based on Abstract Syntax Trees (ASTs). However, they can not well capture both the long-distance and local relations between nodes while retaining the overall structural information of AST. To mitigate this problem, we present to the community a prototype tool called PYSCRIBE, which expands the Transformer model to a new encoder-decoder architecture to incorporate triplet positions into the node-level and edge-level features of AST towards automatically generating comments for Python code. To the best of our knowledge, this paper makes the first attempt to consider the edges of AST as an explicit component for better code representation, as to generate higher qualified comments. By specifying triplet positions for each node and edge, the overall structural information can be well preserved in the learning process. In the decoder, we perform a two-stage decoding process over the extracted node and edge features to generate comments. To evaluate the effectiveness of PYSCRIBE, we resort to a large dataset of code-comment pairs by mining Jupyter notebooks from GitHub, for which we have made it publicly available to support further studies. The evaluation results show that PYSCRIBE is indeed effective, outperforming the state-of-the-art by achieving an average BLEU score (i.e., av-BLEU) of ≈0.28. © 2022, The Authors. All rights reserved.

关键词： Large dataset

LION: Implicit Vision Prompt Tuning

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arXiv 2023年

作者： Wang, Haixin Chang, Jianlong Zhai, Yihang Luo, Xiao Sun, Jinan Lin, Zhouchen Tian, Qi National Engineering Research Center for Software Engineering Peking University China Huawei Cloud & AI China School of Mathematical Sciences Peking University China National Key Lab of General AI School of Intelligence Science and Technology Peking University China Peng Cheng Laboratory China

Despite recent promising performances across a range of vision tasks, vision Transformers still have an issue of high computational costs. Recently, vision prompt learning has provided an economical solution to this problem without fine-tuning the whole large-scale model. However, the efficiency and effectiveness of existing models are still far from satisfactory due to the parameter cost of extensive prompt blocks and tricky prompt framework designs. In this paper, we propose a light-weight prompt framework named impLicit vIsion prOmpt tuNing (LION), which is motivated by deep implicit models with stable low memory costs for various complex tasks. In particular, we merely insert two equilibrium implicit layers in two ends of the pre-trained backbone with parameters frozen. Moreover, according to the lottery hypothesis, we further prune the parameters to relieve the computation burden in implicit layers. Various experiments have validated that our LION obtains promising performances on a wide range of datasets. Most importantly, LION reduces up to 11.5 % of training parameter numbers while obtaining higher performance than the state-of-the-art VPT, especially under challenging scenes. Furthermore, we find that our proposed LION has an excellent generalization performance, making it an easy way to boost transfer learning in the future. Copyright © 2023, The Authors. All rights reserved.

关键词： Transfer learning

Federated Linear Bandit Learning via Over-the-air Computation

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Federated Linear Bandit Learning via Over-the-air Computatio...

IEEE Conference on Global Communications (GLOBECOM)

作者： Jiali Wang Yuning Jiang Xin Liu Ting Wang Yuanming Shi MoE Engineering Research Center of Software/Hardware Co-design Technology and Application Shanghai Key Lab. of Trustworthy Computing East China Normal University China School of Information Science and Technology (SIST) ShanghaiTech University China Automatic Control Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

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OldVisOnline: Curating a Dataset of Historical Visualizations

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arXiv 2023年

作者： Zhang, Yu Jiang, Ruike Xie, Liwenhan Zhao, Yuheng Liu, Can Ding, Tianhong Chen, Siming Yuan, Xiaoru The Department of Computer Science University of Oxford United Kingdom the School of Intelligence Science and Technology Peking University China The School of Data Science Fudan University China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong The Fundamental Software Innovation Lab Huawei Technologies China

With the increasing adoption of digitization, more and more historical visualizations created hundreds of years ago are accessible in digital libraries online. It provides a unique opportunity for visualization and history research. Meanwhile, there is no large-scale digital collection dedicated to historical visualizations. The visualizations are scattered in various collections, which hinders retrieval. In this study, we curate the first large-scale dataset dedicated to historical visualizations. Our dataset comprises 13K historical visualization images with corresponding processed metadata from seven digital libraries. In curating the dataset, we propose a workflow to scrape and process heterogeneous metadata. We develop a semi-automatic labeling approach to distinguish visualizations from other artifacts. Our dataset can be accessed with OldVisOnline, a system we have built to browse and label historical visualizations. We discuss our vision of usage scenarios and research opportunities with our dataset, such as textual criticism for historical visualizations. Drawing upon our experience, we summarize recommendations for future efforts to improve our dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Digital libraries