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检索条件"机构=Hunan Key Laboratory for Service Computing and Novel Software Technology"

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Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment

Container-based data-intensive application scheduling in hyb...

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作者： Tang, Bing Luo, Jincheng Zhang, Jiaming School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China School of Information Engineering Wuhan University of Technology Hubei Wuhan China

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI-based IoT applications based on Docker and container technology in a cloud-edge collaborative environment. Three typical data-intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container-based data-intensive application scheduling framework. Using an attribute-based data-driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS-OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA-III-based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud-edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS-OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%. © 2023 John Wiley & Sons Ltd.

关键词： Edge computing

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars 24

Towards Planning Package Delivery Paths by Exploiting the Cr...

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nie, Hejing Wen, Yiping Li, Tiancai Ouyang, Sipeng Tian, Muyang School of Computer Science and Engineering Hunan University of Science and Technology China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China

ISBN: (纸本)9798350319934

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method. © 2022 IEEE.

关键词： Cost effectiveness

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Cost-Optimized Microservice Deployment for IoT Application in Cloud-Edge Collaborative Environment 26

Cost-Optimized Microservice Deployment for IoT Application i...

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26th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Zhang, Xiaoyuan Tang, Bing Yang, Qing Xu, Wei Guo, Feiyan Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtan411201 China Guangzhou Maritime University School of Information and Communication Engineering Guangzhou510725 China

ISBN: (纸本)9798350331684

With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires suitable strategies to ensure the quality of service for users. However, the existing container orchestration tools cannot flexibly select the best deployment location according to the user's cost budget, and are insufficient in personalized deployment solutions. From the perspective of application providers, this paper considers the location distribution of users, application dependencies, and server price differences, and proposes a genetic algorithm-based Internet-of-Things (IoT) application deployment strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints. This problem is an NP-hard problem, and genetic algorithm is introduced to solve the optimization problem effectively and improve the deployment efficiency. The proposed algorithm is compared with four baseline algorithms, Time-Greedy, Cost-Greedy, Random and PSO, using real datasets and some synthetic datasets. The results show that the proposed algorithm outperforms other competing baseline algorithms. © 2023 IEEE.

关键词： Genetic algorithms

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A Survey of Applications of Vision Transformer and its Variants

A Survey of Applications of Vision Transformer and its Varia...

引用

Intelligent Data and Security (IDS), IEEE International Conference on

作者： Chuang Wu Tingqin He Hunan Key Laboratory for Service computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology

ISBN: (数字)9798350389487

ISBN: (纸本)9798350389494

The Transformer architecture, renowned for its efficacy in natural language processing, encounters unique hur-dles when applied to computer vision. In response, the Vision Transformer (Vi $T$ ) emerges as a successful adaptation for image classification tasks. While ViT exhibits tremendous potential in revolutionizing computer vision, addressing its inherent chal-lenges and limitations stands as a critical endeavor. This compre-hensive survey meticulously scrutinizes the drawbacks associated with ViT, proposing bespoke adaptations tailored to specific applications while showcasing their remarkable performance across diverse visual tasks. Moreover, it delves into the evolution of ViT adaptations across various visual domains, elucidating four promising directions for future research and development in this dynamic field.

关键词： Surveys Computer vision Visualization Computational modeling Transformers Real-time systems Data models

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A One-Dimensional Residual Network and Physical Fitness-Based Exercise Prescription Recommendation Method 10

A One-Dimensional Residual Network and Physical Fitness-Base...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Fan, Runqing Peng, Zhenlian Cao, Buqing Liu, Jianxun Che, Peng Chen, Tieping Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In the context of the national big data strategy, physical fitness test data has become one of the main influencing factors in guiding and promoting the participation of the population in sports and fitness. Recommending exercise prescriptions based on national physical fitness test data has become an important research topic. However, currently, there is little research on how to accurately use computer data processing technology to recommend exercise prescriptions based on physical fitness test data. In this study, we propose a ResNet-based Exercise Prescription (ResNet-EP) method that utilizes one-dimensional residual neural network technology to recommend exercise prescriptions based on physical fitness testing data. This method comprehensively analyzes physical fitness testing data and exercise prescription data and realizes the automatic recommendation of exercise prescriptions. Experimental results on a real dataset demonstrate that the ResNet-EP model outperforms other comparison models in terms of precision (79.98%), recall (83.73%), and F1 score (81.81%). This study provides novel insights into the combination of physical fitness testing and exercise. © 2023 IEEE.

关键词： Machine learning

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A missing physical fitness test data classification method based on MLP 10

A missing physical fitness test data classification method b...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Che, Peng Peng, Zhenlian Cao, Buqing Liu, Jianxun Chen, Tieping Fan, Runqing Hunan University of Science and Technology School of Computer Science and Engineering Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Xiangtan China Hunan University of Science and Technology School of Physical Education Xiangtan China

ISBN: (纸本)9798350312461

In recent years, the physical health status of college students have been attached by the country and all universities. Much effort is spent every year to conduct physical health tests, generating a large amount of physical fitness data. Due to some irresistible reasons, such as entering incorrect data or students being unable to participate in some physical fitness programs, missing data exists in the physical fitness test, and the calculation of total points according to the existing rules of weighted summation may lead to inaccurate classification of some students grades. To this end, a classification method based on physical fitness test data with missing items is proposed in this paper. Firstly, the method collects the raw data set and constructs a structured data set. Then, the MLP method is used to construct a classifier model to classify the test data set with missing items. Finally, compared experiments are conducted with the training model constructed by GaussianNB, SVM and XGBoost and the results show that the classification effect of MLP is excellent, and the accuracy of classification can reach 93.29 %. © 2023 IEEE.

关键词： Classification (of information)

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Application Deployment in Mobile Edge computing Environment Based on Microservice Chain 25

Application Deployment in Mobile Edge Computing Environment ...

引用

25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Li, Haiyan Tang, Bing Xu, Wei Guo, Feiyan Zhang, Xiaoyuan Hunan University of Science and Technology School of Computer Science and Engineering Hunan Laboratory for Service Computing and Novel Software Technology Xiangtan411201 China

ISBN: (纸本)9781665405270

Mobile edge computing (MEC) has become an extremely hot topic in recent years. Mobile edge cloud relies on storage and computing resources on network edge to provide users with delay-sensitive services. However, the transmission delay among microservices and the load of the servers tend to increase due to improper service placement and unreasonable resource allocation under MEC. In this paper, an edge service placement strategy based on an improved fast non-domination sorted genetic algorithm is proposed. First, a microservice placement optimization model is built with the goal of minimizing the average transmission delay and the load balance degree. Then, a genetic algorithm-based microservice placement approach called GA-MSP using improved NSGA-II is studied under the premise that a single service instance is deployed only on one container. The experiments show that the proposed GA-MSP approach is able to achieve low delay and load balance effectively, and ultimately deploy services based on the resulting sets after convergence, which outperforms several other existing representative methods. © 2022 IEEE.

关键词： Mobile edge computing

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A Web service Recommendation Method Based on Adaptive Gate Network and xDeepFM 1

引用

22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Tao, Zhi Cao, Buqing Ye, Hongfan Kang, Guosheng Peng, Zhenlian Wen, Yiping School of Computer Science and Engineering and Hunan Key Laboratory of Service Computing and New Software Service Technology Hunan University of Science and Technology Hunan Xiangtan China School of Computer and Artificial Intelligence Huaihua University Huaihua China

ISBN: (数字)9783031226779

ISBN: (纸本)9783031226762

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential increase in the number of Web services. To automatically generate or recommend a group of Web services according to the user's natural language requirements description, to build a Mashup application to meet the user's requirement is a hot topic in service computing. Some researchers enhance the quality of Web service recommendation by using auxiliary information into the recommendation system. However, they mainly focus on adding external information (e.g., pre-training of external corpora) to enhance semantic features, while some internal statistical features of the corpus such as word distribution on labels and frequency are not fully exploited. Compared to other exterior knowledge, statistical features are naturally compatible with Web service recommendation tasks. To fully exploit the statistical features, this paper proposes a Web service recommendation method based on adaptive gate network and xDeepFM model. In this method, firstly, the description document of Web services is taken as the basic corpus, the semantic and statistical information in the corpus are mined by utilizing the adaptive gate network, and the statistical features are encoded by a variational encoder. Secondly, the similarity between Web services is derived from the semantic features, at the same time the popularity and co-occurrence of Web services are calculated. Thirdly, the xDeepFM model is used to mine the explicit and implicit higher-order interactions in the sparse matrix which consists of the above information to recommend Web services for Mashup application. Finally, a multiple sets of experiments based on the dataset crawled from the ProgrammableWeb have been conducted to evaluate the proposed method and the experimental result shows that t

关键词： Web services

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Operator Placement for IoT Data Streaming Applications in Edge computing Environment 22nd

Operator Placement for IoT Data Streaming Applications in ...

引用

22nd International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2022

作者： Chen, Sixin Tang, Bing Yang, Qing Liu, Yuanyuan School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan411201 China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou510725 China

ISBN: (纸本)9783031226762

With the rapid development of IoT applications, how to process large and continuous data streams in real time for IoT applications has become a challenge in recent years. This paper presents a model to predict the Maximum Sustainable Throughput (MST) of data streaming applications at the edge, and then proposes GACP, a constraint programming-base operator placement strategy to maximize MST using the genetic algorithm. We evaluated the proposed operator placement strategy in terms of MST, the number of edge servers in usage, and the percentage of communications within edge servers. Experimental results show that compared with the existing heuristic algorithms, the proposed GACP strategy can achieve better MST, which is 2–5 times higher than existing algorithms. © 2023, Springer Nature Switzerland AG.

关键词： Edge computing

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SyNet:Medical image anomaly detection with noise synthesis network

SyNet:Medical image anomaly detection with noise synthesis n...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart computing (HPSC) and Intelligent Data and Security (IDS)

作者： Jingnian Liu Wei Liang Yuxiang Chen Kai Jin School of Computer Science and Engineering Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Xiangtang China

ISBN: (数字)9798350389524

ISBN: (纸本)9798350389531

Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to supervised learning not being able to obtain sufficient training. While existing unsupervised learning is mainly based on reconstruction and generation as well as feature embedding, these methods do not take into account the problem of data type bias during domain migration and often face the problem of loose judgment boundaries. To this end, this paper proposes a novel unsupervised learning method, SyNet, based on noisy anomaly synthesis. First, the problem at domain migration is well solved by aggregating the range of features within the feature map, fusing the multiscale features at the intermediate level, and adding feature adapters. Then, anomaly samples are synthesized by adding noise in the feature space, which is more efficient and stable than generating samples in the image space. Finally, normal and abnormal data are distinguished by training the discriminator. In this paper, experiments are conducted on ISIC and brain MRI datasets, and SyNet achieves nearly 20% improvement in AUC, ACC and other metrics compared with the current mainstream methods, and also has good inference efficiency.

关键词： Training Magnetic resonance imaging Noise Supervised learning Security Noise measurement Lesions

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