检索结果-内蒙古大学图书馆

Fast Outage Analysis of Large-Scale Production Clouds with Service Correlation Mining

学校读者我要写书评

暂无评论

Fast Outage Analysis of Large-Scale Production Clouds with S...

International Conference on Software Engineering (ICSE)

作者： Yaohui Wang Guozheng Li Zijian Wang Yu Kang Yangfan Zhou Hongyu Zhang Feng Gao Jeffrey Sun Li Yang Pochian Lee Zhangwei Xu Pu Zhao Bo Qiao Liqun Li Xu Zhang Qingwei Lin School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China School of Electronics Engineering and Computer Science Peking University Beijing China Microsoft Research Beijing China School of Electrical Engineering and Computing The University of Newcastle Australia Microsoft Azure Redmond USA

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%-83.5%, which outperforms the state-of-the-art triage approach by 28.0%-29.7%.

关键词： Cloud computing Root cause analysis Correlation Systematics Production User experience Software engineering

LasHeR: A large-scale high-diversity benchmark for RGBT tracking

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Li, Chenglong Xue, Wanlin Jia, Yaqing Qu, Zhichen Luo, Bin Tang, Jin Sun, Dengdi Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Artificial Intelligence Anhui University Hefei230601 China Information Materials and Intelligent Sensing Laboratory of Anhui Province Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei230601 China

RGBT tracking receives a surge of interest in the computer vision community, but this research field lacks a large-scale and high-diversity benchmark dataset, which is essential for both the training of deep RGBT trackers and the comprehensive evaluation of RGBT tracking methods. To this end, we present a Large-scale High-diversity benchmark for short-term RGBT tracking (LasHeR) in this work. LasHeR consists of 1224 visible and thermal infrared video pairs with more than 730K frame pairs in total. Each frame pair is spatially aligned and manually annotated with a bounding box, making the dataset well and densely annotated. LasHeR is highly diverse capturing from a broad range of object categories, camera viewpoints, scene complexities and environmental factors across seasons, weathers, day and night. We conduct a comprehensive performance evaluation of 12 RGBT tracking algorithms on the LasHeR dataset and present detailed analysis. In addition, we release the unaligned version of LasHeR to attract the research interest for alignment-free RGBT tracking, which is a more practical task in real-world applications. The datasets and evaluation protocols are available at: https://***/mmic-lcl/Datasets-and-benchmark-code. Copyright © 2021, The Authors. All rights reserved.

关键词： Large dataset

Quantum-assited anomaly detection with multivariate Gaussian distribution

学校读者我要写书评

暂无评论

arXiv 2025年

作者： Yu, Chao-Hua Rao, Hong-Miao Wu, Ying-Pei Liu, De-Xi Liu, Xi-Ping Wan, Lin-Chun School of Computing and Artificial Intelligence Jiangxi University of Finance and Economics Nanchang330032 China Jiangxi Provincial Key Laboratory of Multimedia Intelligent Processing Jiangxi University of Finance and Economics Nanchang330032 China School of Computer and Information Science Southwest University Chongqing400715 China

Anomaly detection with multivariate Gaussian distribution, termed Gassian anomaly detection (GAD), is a prominent problem in data mining and machine learning, in which all training data points of a given dataset are assumed to be drawn from an unknown multivariate Gassian distribution and those points with low probability density function values are deemed anomalies. Hence, the central task of GAD is to calculate the probability density function of a new data point by retrieving its mean values and covariance matrix, which could be time-consuming when addressing a large dataset. Recently, several quantum algorithms have been proposed to for GAD, which have been shown significantly faster than the classical counterparts under certain conditions. However, they all require quantum phase estimation as a key subroutine that may have exponentially high quantum circuit depth and is not desirable in the noisy intermediate-scale quantum (NISQ) era. In this paper, we propose a new quantum algorithm for GAD without phase estimation, whose quantum circuit has depth only linear in the number of qubits, which is relatively more friendly on NISQ devices. Specifically, building on arithmetic-free black-box quantum state preparation (AFBBQSP), our quantum algorithm outputs the estimates of the mean values and the covariance matrix both in the classical form, so that anomaly detection of any data point can be done immediately on a classical computer at little cost. It is shown that our quantum algorithm is highly efficient when handling low-dimensional datasets with well-conditioned data matrices. Moreover, unlike prior quantum algorithms for GAD which require the input data to be quantum, mean centered, or feature correlated, our quantum algorithm release these constraints and thus is more data practical. Our work highlights the role of AFBBQSP in bringing quantum machine learning closer to reality. Copyright © 2025, The Authors. All rights reserved.

关键词： Gaussian distribution

A dynamic level scheduling algorithm based on AHP

学校读者我要写书评

暂无评论

UPB Scientific Bulletin, Series C: Electrical Engineering an...

UPB Scientific Bulletin, Series C: Electrical Engineering and Computer science 2020年第1期82卷 117-130页

作者： Jiao, Jintao Yu, Wensen Guo, Lei School of Mathematics and Computer Science Wuyi University China School of Information Management Jiangxi University of Finance and Economics China The Key Laboratory of Cognitive Computing and Intelligent Information Processing of Fujian Education Institutions China

Batch scheduling strategies mainly focus on computing resources allocation in the existing cloud computing environment. Considering the heterogeneity of computing resources and the risk undertaken by computing resources, this paper proposes a dynamic level scheduling algorithm based on AHP (DLSAHP). From the perspective of cloud computing resources providers, the algorithm uses scale 1-9 to fully consider the risk undertaken by computing resources. The experiment results show that DLSAHP can effectively improve the rate of successful tasks execution within deadline, at the same time the profit in one unit of time is also increased. © 2020, Politechnica University of Bucharest. All rights reserved.

关键词： Scheduling algorithms

Global-and-Local Collaborative Learning for Co-Salient Object Detection

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Cong, Runmin Yang, Ning Li, Chongyi Fu, Huazhu Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong Hong Kong The School of Computer Science and Engineering Nanyang Technological University Singapore A*STAR Singapore The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The goal of co-salient object detection (CoSOD) is to discover salient objects that commonly appear in a query group containing two or more relevant images. Therefore, how to effectively extract inter-image correspondence is crucial for the CoSOD task. In this paper, we propose a global-and-local collaborative learning architecture, which includes a global correspondence modeling (GCM) and a local correspondence modeling (LCM) to capture comprehensive inter-image corresponding relationship among different images from the global and local perspectives. Firstly, we treat different images as different time slices and use 3D convolution to integrate all intra features intuitively, which can more fully extract the global group semantics. Secondly, we design a pairwise correlation transformation (PCT) to explore similarity correspondence between pairwise images and combine the multiple local pairwise correspondences to generate the local inter-image relationship. Thirdly, the inter-image relationships of the GCM and LCM are integrated through a global-and-local correspondence aggregation (GLA) module to explore more comprehensive inter-image collaboration cues. Finally, the intra- and inter-features are adaptively integrated by an intra-and-inter weighting fusion (AEWF) module to learn co-saliency features and predict the co-saliency map. The proposed GLNet is evaluated on three prevailing CoSOD benchmark datasets, demonstrating that our model trained on a small dataset (about 3k images) still outperforms eleven state-of-the-art competitors trained on some large datasets (about 8k̴200k images). © 2022, CC BY-NC-SA.

关键词： Convolution

Does Thermal Really Always Matter for RGB-T Salient Object Detection?

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Cong, Runmin Zhang, Kepu Zhang, Chen Zheng, Feng Zhao, Yao Huang, Qingming Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The Department of Computer Science City University of Hong Kong China The Department of Computer Science and Technology Southern University of Science and Technology Shenzhen518055 China The Research Institute of Trustworthy Autonomous Systems Shenzhen518055 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Peng Cheng Laboratory Shenzhen518055 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

In recent years, RGB-T salient object detection (SOD) has attracted continuous attention, which makes it possible to identify salient objects in environments such as low light by introducing thermal image. However, most of the existing RGBT SOD models focus on how to perform cross-modality feature fusion, ignoring whether thermal image is really always matter in SOD task. Starting from the definition and nature of this task, this paper rethinks the connotation of thermal modality, and proposes a network named TNet to solve the RGB-T SOD task. In this paper, we introduce a global illumination estimation module to predict the global illuminance score of the image, so as to regulate the role played by the two modalities. In addition, considering the role of thermal modality, we set up different cross-modality interaction mechanisms in the encoding phase and the decoding phase. On the one hand, we introduce a semantic constraint provider to enrich the semantics of thermal images in the encoding phase, which makes thermal modality more suitable for the SOD task. On the other hand, we introduce a two-stage localization and complementation module in the decoding phase to transfer object localization cue and internal integrity cue in thermal features to the RGB modality. Extensive experiments on three datasets show that the proposed TNet achieves competitive performance compared with 20 state-of-the-art methods. The code and results can be found from the link of https://***/proj_***. © 2022, CC BY-NC-SA.

关键词： Semantics

Abnormal Network Traffic Detection based on Leaf Node Density Ratio 2019

学校读者我要写书评

暂无评论

Abnormal Network Traffic Detection based on Leaf Node Densit...

Proceedings of the 2019 the 9th International Conference on Communication and Network Security

作者： Huajun Liu Zhanghui Liu Yanhua Liu Xiaoling Gao College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China College of Mathematics and Computer Science Fuzhou University Fuzhou Fujian China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou Fujian China

ISBN: (纸本)9781450376624

As the network evolves, cyber-attacks become more and more diverse. In the process of detecting network traffic, the most complicated but also the most important task is to find unknown abnormal network traffic data in time. In the existing abnormal network traffic detection method based on Extended Isolation Forest, there are limitations such as unbalanced detection accuracy and insufficient generalization ability. An improved abnormal network traffic detection method EIF-LNDR is proposed for the above problems. Based on the leaf node density ratio, the anomaly score of the instance can be calculated differently for each iTree. The experiments show that EIF-LNDR has significant improvement in precision, false negative rate, and detector efficiency compared with Extended Isolation Forest and LOF methods.

关键词： leaf node density ratio extended Isolation Forest network traffic data mining cyber attack Abnormal detection

AdAUC: End-to-end Adversarial AUC Optimization Against Long-tail Problems

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

Fast outage analysis of large-scale production clouds with service correlation mining

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Wang, Yaohui Li, Guozheng Wang, Zijian Kang, Yu Zhou, Yangfan Zhang, Hongyu Gao, Feng Sun, Jeffrey Yang, Li Lee, Pochian Xu, Zhangwei Zhao, Pu Qiao, Bo Li, Liqun Zhang, Xu Lin, Qingwei School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing China School of Electronics Engineering and Computer Science Peking University Beijing China Microsoft Research Beijing China School of Electrical Engineering and Computing The University of Newcastle Australia Microsoft Azure Redmond United States

Cloud-based services are surging into popularity in recent years. However, outages, i.e., severe incidents that always impact multiple services, can dramatically affect user experience and incur severe economic losses. Locating the root-cause service, i.e., the service that contains the root cause of the outage, is a crucial step to mitigate the impact of the outage. In current industrial practice, this is generally performed in a bootstrap manner and largely depends on human efforts: the service that directly causes the outage is identified first, and the suspected root cause is traced back manually from service to service during diagnosis until the actual root cause is found. Unfortunately, production cloud systems typically contain a large number of interdependent services. Such a manual root cause analysis is often time-consuming and labor-intensive. In this work, we propose COT, the first outage triage approach that considers the global view of service correlations. COT mines the correlations among services from outage diagnosis data. After learning from historical outages, COT can infer the root cause of emerging ones accurately. We implement COT and evaluate it on a real-world dataset containing one year of data collected from Microsoft Azure, one of the representative cloud computing platforms in the world. Our experimental results show that COT can reach a triage accuracy of 82.1%∼83.5%, which outperforms the state-of-the-art triage approach by 28.0%∼29.7%. Copyright © 2021, The Authors. All rights reserved.

关键词： Cloud computing