Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Chen, Feiyi"'
Autor:
Chen, Feiyi, Zhang, Yingying, Fan, Lunting, Liang, Yuxuan, Pang, Guansong, Wen, Qingsong, Deng, Shuiguang
Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, cons
Externí odkaz:
http://arxiv.org/abs/2408.04236
Autor:
Chen, Feiyi, zhang, Yingying, Qin, Zhen, Fan, Lunting, Jiang, Renhe, Liang, Yuxuan, Wen, Qingsong, Deng, Shuiguang
Anomaly detection significantly enhances the robustness of cloud systems. While neural network-based methods have recently demonstrated strong advantages, they encounter practical challenges in cloud environments: the contradiction between the imprac
Externí odkaz:
http://arxiv.org/abs/2311.16191
Autor:
Chen, Feiyi, Qin, Zhen, Zhang, Yingying, Deng, Shuiguang, Xiao, Yi, Pang, Guansong, Wen, Qingsong
Most of current anomaly detection models assume that the normal pattern remains same all the time. However, the normal patterns of Web services change dramatically and frequently. The model trained on old-distribution data is outdated after such chan
Externí odkaz:
http://arxiv.org/abs/2310.05668
Existing approaches defend against backdoor attacks in federated learning (FL) mainly through a) mitigating the impact of infected models, or b) excluding infected models. The former negatively impacts model accuracy, while the latter usually relies
Externí odkaz:
http://arxiv.org/abs/2309.16456
Cloud providers can greatly benefit from accurate workload prediction. However, the workload of cloud servers is highly variable, with occasional heavy workload bursts. This makes workload prediction challenging. There are mainly two categories of wo
Externí odkaz:
http://arxiv.org/abs/2308.01917
Autor:
Deng, Shuiguang, Zhao, Hailiang, Huang, Binbin, Zhang, Cheng, Chen, Feiyi, Deng, Yinuo, Yin, Jianwei, Dustdar, Schahram, Zomaya, Albert Y.
The development of cloud computing delivery models inspires the emergence of cloud-native computing. Cloud-native computing, as the most influential development principle for web applications, has already attracted increasingly more attention in both
Externí odkaz:
http://arxiv.org/abs/2306.14402
LCE-Calib: Automatic LiDAR-Frame/Event Camera Extrinsic Calibration With A Globally Optimal Solution
The combination of LiDARs and cameras enables a mobile robot to perceive environments with multi-modal data, becoming a key factor in achieving robust perception. Traditional frame cameras are sensitive to changing illumination conditions, motivating
Externí odkaz:
http://arxiv.org/abs/2303.09825
Inspired by the fact that humans use diverse sensory organs to perceive the world, sensors with different modalities are deployed in end-to-end driving to obtain the global context of the 3D scene. In previous works, camera and LiDAR inputs are fused
Externí odkaz:
http://arxiv.org/abs/2207.00186
Autor:
Zhao, Hailiang, Deng, Shuiguang, Chen, Feiyi, Yin, Jianwei, Dustdar, Schahram, Zomaya, Albert Y.
Multi-server jobs are imperative in modern cloud computing systems. A noteworthy feature of multi-server jobs is that, they usually request multiple computing devices simultaneously for their execution. How to schedule multi-server jobs online with a
Externí odkaz:
http://arxiv.org/abs/2204.04371
Autor:
Huang, Rulu, Wang, Yue, Chen, Feiyi, Liu, Huai, Zhang, Rui, Jia, Wenlong, Peng, Lincai, Sun, Yong, Zhang, Junhua
Publikováno v:
In Chemical Engineering Journal 1 October 2024 497