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Microservice Auto-Scaling Algorithm Based on Workload Prediction in Cloud-Edge Collaboration Environment

Microservice Auto-Scaling Algorithm Based on Workload Predic...

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zijun Peng Bing Tang Wei Xu Qing Yang Ehsanullah Hussaini Yuqiang Xiao Haiyan Li School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China Center of Network and Education Technology Guangzhou Maritime University Guangzhou China

Cloud computing centrally consolidates hardware and computing resources, offering efficient and cost-effective services. However, as cloud computing centers are predominantly built and operated in a fully centralized fashion, the increased distance between these centers and users can lead to a decline in service quality. Real-time interaction and high business continuity are crucial in scenarios like traffic monitoring, AR/VR applications, and the Internet of Things (IoT). Edge computing is better suited to meet the demands of such latency-sensitive business needs. By analyzing and processing massive data directly at edge computing nodes, which focus on network edge devices, reliance on transmission resources is reduced, consequently improving the overall quality and performance of services. Nevertheless, resource-constrained edge nodes require efficient utilization of available infrastructure capacity to ensure specific service level objectives (SLO) for applications. Therefore, this paper introduces XScale, a cloud-edge collaborative system that enables microservices to adaptively scale elastically. XScale applies a Bi-LSTM with an attention mechanism to forecast the workload of microservices. When combined with mechanisms designed to handle burst traffic and a cloud-edge collaborative load forwarding strategy, it achieves both adaptive elastic scaling and proactive load forwarding. Experimental results, obtained using real-world microservice workloads, indicate that the XScale system can significantly reduce SLO violations by 88%, increase resource utilization by 15%, and decrease average response time by 21% when compared to existing advanced reactive scaling methods.

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OcTr: Octree-based Transformer for 3D Object Detection

arXiv

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arXiv 2023年

作者： Zhou, Chao Zhang, Yanan Chen, Jiaxin Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this paper, we propose an Octree-based Transformer, named OcTr, to address this issue. It first constructs a dynamic octree on the hierarchical feature pyramid through conducting self-attention on the top level and then recursively propagates to the level below restricted by the octants, which captures rich global context in a coarse-to-fine manner while maintaining the computational complexity under control. Furthermore, for enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask, to fully exploit semantic and geometry clues. Extensive experiments are conducted on the Waymo Open Dataset and KITTI Dataset, and OcTr reaches newly state-of-the-art results. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Deep Learning-Enabled Anomaly Detection for IoT Systems

SSRN

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SSRN 2022年

作者： Abusitta, Adel de Carvalho, Glaucio Haroldo Silva Wahab, Omar Abdel Halabi, Talal Fung, Benjamin C.M. Mamoori, Saja Al Department of Computer Science University of Windsor WindsorON Canada Department of Computer Science & Engineering Brock University St. CatharinesON Canada Department of Computer Engineering and Software Engineering Polytechnique Montreal QC Canada Department of Computer Science Laval University QC Canada School of Information Studies McGill University MontrealQC Canada

Internet of Things (IoT) systems have become an intrinsic technology in various industries and government services. Unfortunately, IoT devices and networks are known to be highly vulnerable to security attacks that target data integrity and service availability. Moreover, the heterogeneity of the data collected from various IoT devices, together with the disturbances incurred within the IoT system, render the detection of anomalous behavior and compromised nodes more challenging compared to traditional Information Technology (IT) networks. As a result, there is a pressing need for effective and reliable anomaly detection to identify malicious data to guarantee that they will not be used in IoT-driven decision support systems. In this paper, we propose a deep learning-powered anomaly detection for IoT that can learn and capture robust and useful features, which cannot be significantly affected by unstable environments. These features are then used by the classifier to enhance the accuracy of detecting malicious IoT data. More specifically, the proposed deep learning model is designed based on a denoising autoencoder, which is adopted to obtain features that are robust against the heterogeneous environment of IoT. Experimental results based on real-life IoT datasets show the effectiveness of the proposed framework in terms of enhancing the accuracy of detecting malicious data compared to the state-of-the-art IoT-based anomaly detection models. © 2022, The Authors. All rights reserved.

关键词： Internet of things

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DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration

arXiv

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arXiv 2023年

作者： Zhou, Nan Chen, Jiaxin Huang, Di State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

The visual models pretrained on large-scale benchmarks encode general knowledge and prove effective in building more powerful representations for downstream tasks. Most existing approaches follow the fine-tuning paradigm, either by initializing or regularizing the downstream model based on the pretrained one. The former fails to retain the knowledge in the successive fine-tuning phase, thereby prone to be over-fitting, and the latter imposes strong constraints to the weights or feature maps of the downstream model without considering semantic drift, often incurring insufficient optimization. To deal with these issues, we propose a novel fine-tuning framework, namely distribution regularization with semantic calibration (DR-Tune). It employs distribution regularization by enforcing the downstream task head to decrease its classification error on the pretrained feature distribution, which prevents it from over-fitting while enabling sufficient training of downstream encoders. Furthermore, to alleviate the interference by semantic drift, we develop the semantic calibration (SC) module to align the global shape and class centers of the pretrained and downstream feature distributions. Extensive experiments on widely used image classification datasets show that DR-Tune consistently improves the performance when combing with various backbones under different pretraining strategies. Code is available at: https://***/weeknan/DR-Tune. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration

DR-Tune: Improving Fine-tuning of Pretrained Visual Models b...

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International Conference on computer Vision (ICCV)

作者： Nan Zhou Jiaxin Chen Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

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A Phase Estimation Algorithm for Quantum Speed-Up Multi-Party Computing

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computers, Materials & Continua 2021年第4期67卷 241-252页

作者： Wenbin Yu Hao Feng Yinsong Xu Na Yin Yadang Chen Zhi-Xin Yang Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET) Jiangsu Engineering Center of Network MonitoringSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering Michigan State UniversityEast Lansing48824MIUSA State Key Laboratory of Internet of Things for Smart City and Department of Electromechanical Engineering University of Macao999078Macao

Security and privacy issues have attracted the attention of researchers in the field of IoT as the information processing scale grows in sensor *** computing,theoretically known as an absolutely secure way to store and transmit information as well as a speed-up way to accelerate local or distributed classical algorithms that are hard to solve with polynomial complexity in computation or *** this paper,we focus on the phase estimation method that is crucial to the realization of a general multi-party computing model,which is able to be accelerated by quantum algorithms.A novel multi-party phase estimation algorithm and the related quantum circuit are proposed by using a distributed Oracle operator with *** proved theoretical communication complexity of this algorithm shows it can give the phase estimation before applying multi-party computing efficiently without increasing any additional ***,a practical problem of multi-party dating investigated shows it can make a successful estimation of the number of solution in advance with zero communication complexity by utilizing its special statistic *** simulations present the correctness,validity and efficiency of the proposed estimation method.

关键词： Edge computing security multi-party computing quantum algorithm phase estimation communication complexity

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OcTr: Octree-Based Transformer for 3D Object Detection

OcTr: Octree-Based Transformer for 3D Object Detection

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Chao Zhou Yanan Zhang Jiaxin Chen Di Huang State Key Laboratory of Software Development Environment Beihang University Beijing China School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

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FedCPSO: Federated Learning with Combined Particle Swarm Optimization

FedCPSO: Federated Learning with Combined Particle Swarm Opt...

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Chinese Automation Congress (CAC)

作者： Hongjian Shi Ruhui Ma Haibing Guan Weishan Zhang School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao China

The development of the Internet of Things (IoT) has allowed devices to collect massive amounts of data, and Artificial Intelligence (AI) provides the ability to analyze those data. Moreover, researchers adopt Distributed Machine Learning (DML) methods to train neural networks collaboratively using different users' data. However, DML suffers from privacy issues, and Federated Learning (FL) has been an effective solution. FL transfers the model instead of the data to protect privacy, but the trained models have low accuracies over local datasets due to statistical heterogeneity. Thus, personalized FL (pFL) algorithms have been proposed to handle such heterogeneous data distribution. However, the communication overhead in the pFL algorithms is significant as it requires transmitting additional information. Thus, we propose Federated Learning with Com-bined Particle Swarm Optimization (FedCPSO) in this paper. FedCPSO replaces the aggregation process of FL algorithms with PSO, and we design a velocity in PSO specifically for FL algorithms, using the best global model, the best client models, and the best neighbor models. In addition, we also implement magnitude pruning to reduce the communication volume. The experimental results illustrate that FedCPSO can reduce up to 50% communication volume while having less than a 2% accuracy drop compared with the State-of-the-art (SOTA) pFL algorithm.

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Efficient Region-Aware Neural Radiance Fields for High-Fidelity Talking Portrait Synthesis

arXiv

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arXiv 2023年

作者： Li, Jiahe Zhang, Jiawei Bai, Xiao Zhou, Jun Gu, Lin School of Computer Science and Engineering State Key Laboratory of Software Development Environment Jiangxi Research Institute Beihang University China School of Information and Communication Technology Griffith University Australia RIKEN AIP Japan The University of Tokyo Japan

This paper presents ER-NeRF, a novel conditional Neural Radiance Fields (NeRF) based architecture for talking portrait synthesis that can concurrently achieve fast convergence, real-time rendering, and state-of-the-art performance with small model size. Our idea is to explicitly exploit the unequal contribution of spatial regions to guide talking portrait modeling. Specifically, to improve the accuracy of dynamic head reconstruction, a compact and expressive NeRF-based Tri-Plane Hash Representation is introduced by pruning empty spatial regions with three planar hash encoders. For speech audio, we propose a Region Attention Module to generate region-aware condition feature via an attention mechanism. Different from existing methods that utilize an MLP-based encoder to learn the cross-modal relation implicitly, the attention mechanism builds an explicit connection between audio features and spatial regions to capture the priors of local motions. Moreover, a direct and fast Adaptive Pose Encoding is introduced to optimize the head-torso separation problem by mapping the complex transformation of the head pose into spatial coordinates. Extensive experiments demonstrate that our method renders better high-fidelity and audio-lips synchronized talking portrait videos, with realistic details and high efficiency compared to previous methods. Code is available at https://***/Fictionarry/ ER-NeRF. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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Web-based Automatic Deep Learning Service Generation System by Ontology Technologies

Web-based Automatic Deep Learning Service Generation System ...

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IEEE International Conference on Computational science and engineering, CSE

作者： Incheon Paik Kungan Zeng Munhan Bae School of Computer Science The University of Aizu Aizu-Wakamatsu City Fukushima Japan School of Software and Engineering Sun Yat-sen University Guangzhou China Dept. of Computer Science Mongolia International University Ulaanbaatar Mongolia

Although deep learning (DL) has obtained great achievements in the industry, the involvement of artificial intelligence (AI) experts in developing customized DL services raises high costs and hinders its wide application in the business domain. In this research, a Web-based automatic DL service generation system is presented to address the problem. The system can generate customized DL services without involving AI experts. The main principle of the system adopts ontology technologies to organize DL domain knowledge and generate target services based on the user's requests posted from the front-end web page. In the empirical study, the whole scenario of the system is demonstrated, and the scalability is also evaluated. The result shows that our system can generate customized services correctly and has good scalability.

关键词： Deep learning Knowledge engineering Industries Costs Scientific computing Scalability Web pages

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