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50th International Conference on Very Large Data Bases, VLDB 2024

作者： Qiu, Xiangfei Hu, Jilin Zhou, Lekui Wu, Xingjian Du, Junyang Zhang, Buang Guo, Chenjuan Zhou, Aoying Jensen, Christian S. Sheng, Zhenli Yang, Bin East China Normal University China Huawei Cloud Algorithm Innovation Lab China Aalborg University Denmark

Time series are generated in diverse domains such as economic, traffic, health, and energy, where forecasting of future values has numerous important applications. Not surprisingly, many forecasting methods are being proposed. To ensure progress, it is essential to be able to study and compare such methods empirically in a comprehensive and reliable manner. To achieve this, we propose TFB, an automated benchmark for Time Series Forecasting (TSF) methods. TFB advances the state-of-the-art by addressing shortcomings related to datasets, comparison methods, and evaluation pipelines: 1) insufficient coverage of data domains, 2) stereotype bias against traditional methods, and 3) inconsistent and inflexible pipelines. To achieve better domain coverage, we include datasets from 10 different domains: traffic, electricity, energy, the environment, nature, economic, stock markets, banking, health, and the web. We also provide a time series characterization to ensure that the selected datasets are comprehensive. To remove biases against some methods, we include a diverse range of methods, including statistical learning, machine learning, and deep learning methods, and we also support a variety of evaluation strategies and metrics to ensure a more comprehensive evaluations of different methods. To support the integration of different methods into the benchmark and enable fair comparisons, TFB features a flexible and scalable pipeline that eliminates biases. Next, we employ TFB to perform a thorough evaluation of 21 Univariate Time Series Forecasting (UTSF) methods on 8,068 univariate time series and 14 Multivariate Time Series Forecasting (MTSF) methods on 25 datasets. The results offer a deeper understanding of the forecasting methods, allowing us to better select the ones that are most suitable for particular datasets and settings. Overall, TFB and this evaluation provide researchers with improved means of designing new TSF methods. © 2024, VLDB Endowment. All rights reserve

关键词： Pipelines

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Lifelong 3D Mapping Framework for Hand-held & Robot-mounted LiDAR Mapping Systems

arXiv

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arXiv 2025年

作者： Yang, Liudi Prakhya, Sai Manoj Zhu, Senhua Liu, Ziyuan Intelligent Cloud Technologies Lab Huawei Munich Germany Algorithm Innovation lab Huawei Cloud China

We propose a lifelong 3D mapping framework that is modular, cloud-native by design and more importantly, works for both hand-held and robot-mounted 3D LiDAR mapping systems. Our proposed framework comprises of dynamic point removal, multi-session map alignment, map change detection and map version control. First, our sensor-setup agnostic dynamic point removal algorithm works seamlessly with both hand-held and robot-mounted setups to produce clean static 3D maps. Second, the multi-session map alignment aligns these clean static maps automatically, without manual parameter fine-tuning, into a single reference frame, using a two stage approach based on feature descriptor matching and fine registration. Third, our novel map change detection identifies positive and negative changes between two aligned maps. Finally, the map version control maintains a single base map that represents the current state of the environment, and stores the detected positive and negative changes, and boundary information. Our unique map version control system can reconstruct any of the previous clean session maps and allows users to query changes between any two random mapping sessions, all without storing any input raw session maps, making it very unique. Extensive experiments are performed using hand-held commercial LiDAR mapping devices and open-source robot-mounted LiDAR SLAM algorithms to evaluate each module and the whole 3D lifelong mapping framework. © 2025, CC BY-NC-SA.

关键词： Change detection

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TFB: Towards Comprehensive and Fair Benchmarking of Time Series Forecasting Methods

arXiv

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arXiv 2024年

关键词： Pipelines

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Online Midgress-Sensitive Traffic Allocation for Percentile Charging in Pracitcal CDNs 31

Online Midgress-Sensitive Traffic Allocation for Percentile ...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Zhan, Huiyou Tan, Haisheng Xu, Huang Zhang, Chi Ni, Hongqiu Zhang, Pengfei Shan, Weihua Li, Xiang-Yang University of Science and Technology of China Cas Key Lab of Wireless-Optical Communications China USTC-Deqing Alpha Innovation Research Institute Zhejiang China Huawei Cloud Computing Technologies Co. Ltd. Algorithm Innovation Lab Xi'an China

ISBN: (纸本)9798350399738

The traffic bandwidth costs comprise a significant amount of operating expenditure in CDNs, induced by the traffic from the end-users to edge servers (edge cost) and from the edge to the center servers (midgress cost). Traffic allocation is the main approach to minimizing the total bandwidth cost. The joint optimization of the total costs is challenging, specifically when the percentile charging mechanism as well as some other practical issues are considered, such as the dynamicity of midgress traffic and the granularity of traffic allocation. In this work, based on our novel miss ratio prediction mechanism, we propose the first online framework, named Iris, jointly optimizing the edge and midgress costs under the 95th percentile charging in commercial CDNs. Iris can theoretically achieve a competitive ratio of 1+pe pc, when the miss ratio of all domains is set as b. Here pe and pc are the unit bandwidth price of the edge and midgress cost, respectively. Iris is tolerant to prediction errors which can be deployed in practical CDN systems. Extensive experiments based on real data indicate that Iris can dramatically reduce bandwidth costs by about 8.149% compared with the SOTA schemes, potentially saving millions of dollars per month for our large-scale commercial CDN collaborator. © 2023 IEEE.

关键词： Bandwidth

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Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey 14

Human 3D Avatar Modeling with Implicit Neural Representation...

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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Sun, Mingyang Yang, Dingkang Kou, Dongliang Jiang, Yang Shan, Weihua Yan, Zhe Zhang, Lihua Fudan University Academy for Engineering & Technology Shanghai China Huawei Cloud Computing Technologies Co. Ltd Algorithm Innovation Lab Xi'an China

ISBN: (纸本)9798350336313

A human 3D avatar is one of the important elements in the metaverse, and the modeling effect directly affects people's visual experience. However, the human body has a complex topology and diverse details, so it is often expensive, time-consuming, and laborious to build a satisfactory model. Recent studies have proposed a novel method, implicit neural representation, which is a continuous representation method and can describe objects with arbitrary topology at arbitrary resolution. Researchers applied implicit neural representation to human 3D avatar modeling and obtained more excellent results than traditional methods. This paper comprehensively reviews the application of implicit neural representation in human body modeling. First, we introduce three implicit representations of occupancy field, SDF, and NeRF, and make a classification of the literature investigated in this paper. Then the application of implicit modeling methods in the body, hand, and head are compared and analyzed respectively. Finally, we point out the shortcomings of current work and provide available suggestions for researchers. © 2022 IEEE.

关键词： Topology

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Scalable Reinforcement Learning for Virtual Machine Scheduling

arXiv

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arXiv 2025年

作者： Sheng, Junjie Wu, Jiehao Cui, Haochuan Hu, Yiqiu Zhou, Wenli Zhu, Lei Peng, Qian Li, Wenhao Wang, Xiangfeng Algorithm Innovation Lab Huawei Cloud Huawei Technologies Co. Ltd China School of Computer Science and Technology East China Normal University China Alkaid Lab Huawei Cloud Huawei Technologies Co. Ltd China School of Computer Science and Technology Tongji University China

Recent advancements in reinforcement learning (RL) have shown promise for optimizing virtual machine scheduling (VMS) in small-scale clusters. The utilization of RL to large-scale cloud computing scenarios remains notably constrained. This paper introduces a scalable RL framework, called Cluster Value Decomposition Reinforcement Learning (CVD-RL), to surmount the scalability hurdles inherent in large-scale VMS. The CVD-RL framework innovatively combines a decomposition operator with a look-ahead operator to adeptly manage representation complexities, while complemented by a Top-k filter operator that refines exploration efficiency. Different from existing approaches limited to clusters of 10 or fewer physical machines (PMs), CVD-RL extends its applicability to environments encompassing up to 50 PMs. Furthermore, the CVD-RL framework demonstrates generalization capabilities that surpass contemporary SOTA methodologies across a variety of scenarios in empirical studies. This breakthrough not only showcases the framework’s exceptional scalability and performance but also represents a significant leap in the application of RL for VMS within complex, large-scale cloud infrastructures. The code is available at https://***/r/marl4sche-D0FE. © 2025, CC BY.

关键词： Reinforcement learning

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ReAssigner: A Plug-and-Play Virtual Machine Scheduling Intensifier for Heterogeneous Requests

ReAssigner: A Plug-and-Play Virtual Machine Scheduling Inten...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Cui, Haochuan Sheng, Junjie Jin, Bo Hu, Yiqiu Su, Li Zhu, Lei Zhou, Wenli Wang, Xiangfeng East China Normal University School of Computer Science and Technology Shanghai200062 China Huawei Cloud Computing Technologies Co. Algorithm Innovation Lab. Xi'an710049 China Huawei Cloud Computing Technologies Co. Computing Service Product Dept. Xi'an710049 China

ISBN: (纸本)9781665480451

With the rapid development of cloud computing, virtual machine scheduling has become one of the most important but challenging issues for the cloud computing community, especially for practical heterogeneous request sequences. By analyzing the impact of request heterogeneity on some popular heuristic schedulers, it can be found that existing scheduling algorithms can not handle the request heterogeneity properly and efficiently. In this paper, a plug-and-play virtual machine scheduling intensifier, called Resource Assigner (ReAssigner), is proposed to enhance the scheduling efficiency of any given scheduler for heterogeneous requests. The key idea of ReAssigner is to pre-assign roles to physical resources and let resources of the same role form a virtual cluster to handle homogeneous requests. ReAssigner can cooperate with arbitrary schedulers by restricting their scheduling space to virtual clusters. With evaluations on the real dataset from huawei cloud, the proposed ReAssigner achieves significant scheduling performance improvement compared with some state-of-the-art scheduling methods. © 2022 IEEE.

关键词： cloud computing

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Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey

arXiv

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arXiv 2023年

作者： Sun, Mingyang Yang, Dingkang Kou, Dongliang Jiang, Yang Shan, Weihua Yan, Zhe Zhang, Lihua Academy for Engineering and Technology Fudan University China Algorithm Innovation Lab Huawei Cloud Computing Technologies Co. Ltd Xi'An China

A human 3D avatar is one of the important elements in the metaverse, and the modeling effect directly affects people's visual experience. However, the human body has a complex topology and diverse details, so it is often expensive, time-consuming, and laborious to build a satisfactory model. Recent studies have proposed a novel method, implicit neural representation, which is a continuous representation method and can describe objects with arbitrary topology at arbitrary resolution. Researchers have applied implicit neural representation to human 3D avatar modeling and obtained more excellent results than traditional methods. This paper comprehensively reviews the application of implicit neural representation in human body modeling. First, we introduce three implicit representations of occupancy field, SDF, and NeRF, and make a classification of the literature investigated in this paper. Then the application of implicit modeling methods in the body, hand, and head are compared and analyzed respectively. Finally, we point out the shortcomings of current work and provide available suggestions for researchers. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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Online Midgress-Sensitive Traffic Allocation for Percentile Charging in Pracitcal CDNs

Online Midgress-Sensitive Traffic Allocation for Percentile ...

引用

International Workshop on Quality of Service

作者： Huiyou Zhan Haisheng Tan Huang Xu Chi Zhang Hongqiu Ni Pengfei Zhang Weihua Shan Xiang-Yang Li CAS Key Lab of Wireless-Optical Communications University of Science and Technology of China China USTC-Deqing Alpha Innovation Research Institute Zhejiang China Algorithm Innovation Lab Huawei Cloud Computing Technologies Co. Ltd. Xi'an China

The traffic bandwidth costs comprise a significant amount of operating expenditure in CDNs, induced by the traffic from the end-users to edge servers (edge cost) and from the edge to the center servers (midgress cost). Traffic allocation is the main approach to minimizing the total bandwidth cost. The joint optimization of the total costs is challenging, specifically when the percentile charging mechanism as well as some other practical issues are considered, such as the dynamicity of midgress traffic and the granularity of traffic allocation. In this work, based on our novel miss ratio prediction mechanism, we propose the first online framework, named Iris, jointly optimizing the edge and midgress costs under the 95th percentile charging in commercial CDNs. Iris can theoretically achieve a competitive ratio of $1+\frac{p_{e}}{\beta\cdot p_{c}}$ , when the miss ratio of all domains is set as $\beta$ . Here $p_{e}$ and $p_{c}$ are the unit bandwidth price of the edge and midgress cost, respectively. Iris is tolerant to prediction errors which can be deployed in practical CDN systems. Extensive experiments based on real data indicate that Iris can dramatically reduce bandwidth costs by about 8.149% compared with the SOTA schemes, potentially saving millions of dollars per month for our large-scale commercial CDN collaborator.

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Human 3D Avatar Modeling with Implicit Neural Representation: A Brief Survey

Human 3D Avatar Modeling with Implicit Neural Representation...

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International Conference on Signal Processing Systems, ICSPS

作者： Mingyang Sun Dingkang Yang Dongliang Kou Yang Jiang Weihua Shan Zhe Yan Lihua Zhang Academy for Engineering & Technology Fudan University Shanghai China Algorithm Innovation Lab Huawei Cloud Computing Technologies Co. Ltd Xi'an China

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