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Container Scheduling in Hybrid Cloud-Edge Collaborative System

Container Scheduling in Hybrid Cloud-Edge Collaborative Syst...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

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

ISBN: (纸本)9781665435413

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 paper 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 paper 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. This paper introduces the implementation of the prototype system and builds a cloud-edge collaborative test platform consisting of Raspberry Psi and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing scheduling approaches in improving the utilization rate of edge resources and saving cost of cloud resources.

关键词： Training Performance evaluation Cloud computing Job shop scheduling Collaboration Systems architecture Prototypes

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

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

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International Conference on Computer Supported Cooperative Work in Design

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

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.

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LightNestle: Quick and Accurate Neural Sequential Tensor Completion via Meta Learning

LightNestle: Quick and Accurate Neural Sequential Tensor Com...

引用

IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Yuhui Li Wei Liang Kun Xie Dafang Zhang Songyou Xie KuanChing Li College of Computer Science and Electronic Engineering Hunan University Changsha China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

Network operation and maintenance rely heavily on network traffic monitoring. Due to the measurement overhead reduction, lack of measurement infrastructure, and unexpected transmission error, network traffic monitoring systems suffer from incomplete observed data and high data sparsity problems. Recent studies model missing data recovery as a tensor completion task and show good performance. Although promising, the current tensor completion models adopted in network traffic data recovery lack an effective and efficient retraining scheme to adapt to newly arrived data while retaining historical information. To solve the problem, we propose LightNestle, a novel sequential tensor completion scheme based on meta-learning, which designs (1) an expressive neural network to transfer spatial knowledge from previous embeddings to current embeddings; (2) an attention-based module to transfer temporal patterns into current embeddings in linear complexity; and (3) meta-learning-based algorithms to iteratively recover missing data and update transfer modules to catch up with learned knowledge. We conduct extensive experiments on two real-world network traffic datasets to assess our performance. Results show that our proposed methods achieve both fast retraining and high recovery accuracy.

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Blockchain-based Distributed Storage System for Task Offloading in Vehicular Edge computing

Blockchain-based Distributed Storage System for Task Offload...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yutian Zhang Bing Tang Qing Yang Xiaoyuan Zhang Jincheng Luo Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China

With the deepening requirements of development for the Internet of Vehicles technology, intelligent vehicles need to complete the complex task in a fast response time to meet the needs, which brings great challenges to reliable vehicle service provision. In order to solve this problem, we propose a task offloading scheme into the Internet of Vehicles, which can reduce the execution time of tasks. In this paper, we introduce a location-aware distributed storage system called LDSS to support task offloading. Data can be written, stored, computed and read out through different mobile edge servers. Firstly, the identity of the vehicle is stored on the blockchain, and then the vehicle needs to offload the data to the nearest mobile edge server. Then, the storage node and computing node are selected based on the time optimization strategy. Finally, by predicting the location of the vehicle, the mobile edge server nearest to the predicted vehicle location is selected as the access node for result downloading. After validating the identity of vehicle based on blockchain technology, the output result is returned to the vehicle. The experiments show that our proposed task offloading scheme based on blockchain can effectively achieve the effect of low latency and faster task response, as well as ensure data security.

关键词： Blockchains Servers Reliability Time factors Task analysis Low latency communication Optimization

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An Efficient Authentication Protocol for Brand Cosmetics Anti-Counterfeiting System

An Efficient Authentication Protocol for Brand Cosmetics Ant...

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

作者： Xiangwei Meng Qingchun Yu Wei Lang Yufeng Liang Zisang Xu Kuanching Li Nanjing University of Aeronautics and Astronautics Nanjing China Hunan University of Science and Technology Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China Changsha University of Science and Technology Changsha China

The counterfeit brand cosmetics made available on the market have always been a widespread concern. Traditional anti-counterfeiting schemes usually focus on the authenticity of brand cosmetics. However, verifying the identity of the sales counter is also a challenging issue to be solved. In this paper, we propose a new authentication protocol for brand cosmetics anti-counterfeiting system which is used for checking the identity of sales counter and the authenticity of brand cosmetics. The sales counter authenticated by the anticounterfeiting server obtains a cosmetic authentication confirmation about the authenticity of the brand cosmetic. Besides, the security of proposed protocol is proved by informally analysis. Performance evaluation shows that the proposed protocol is efficiency.

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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...

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

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

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

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

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.

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A missing physical fitness test data classification method based on MLP

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

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IEEE International Conference on Cyber Security and Cloud computing (CSCloud)

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

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 %.

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Robust and Unsupervised KPI Anomaly Detection Based on Highly Sensitive Conditional Variational Auto-Encoders

Robust and Unsupervised KPI Anomaly Detection Based on Highl...

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Shili Yan Bing Tang Qing Yang Yijia He Xiaoyuan Zhang Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangzhou China Beijing Jiaotong University Beijing China

To ensure the stability and reliability of service quality, large Internet companies need to closely monitor various KPIs (key Performance Indicators, such as network throughput, CPU usages) and trigger timely troubleshooting or mitigation when any anomaly occurs. However, the diversity and complexity of anomalies bring great challenges to this work, especially when there is no manual label and low delay is required. In this paper, we propose HS-VAE (Highly Sensitive Variational Auto-Encoders), an unsupervised, robust algorithm based on CVAE (Conditional Variational Auto-Encoders) with high sensitivity to anomalies for KPIs, which contains mainly 3 parts: a simple but important data filter before training, improved conditional VAE with two dropout layers and adjusted anomaly detection method based on reconstruction probability. Our experiments using real-world data show that, HS-VAE's best F1-score ranges from 0.91 to 0.98. In addition, HS- VAE is excellent in sensitivity to anomaly and works well even with low latency requirements.

关键词： Training Sensitivity key performance indicator Training data Manuals Throughput Reliability

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A Multiple-Path Learning Neural Network Model for Code Completion

A Multiple-Path Learning Neural Network Model for Code Compl...

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IEEE International Conference on Web services (ICWS)

作者： Yi Liu Jianxun Liu Xiangping Zhang Haize Hu Hunan Provincial Key Laboratory for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Informationn Hunan University of Humanities Science and Technology Loudi China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China

Code completion, which can accelerate the software development process and improve the quality of software products, is an essential part of today’s integrated development environments. It has become an important research topic in the field of software engineering. Recent studies have shown that the method of code completion based on the Abstract Syntax Tree (AST) learns syntactic information about the code, which helps to improve the accuracy of code completion. However, when modeling neural networks for ASTs, the sequencing operation of nodes leads to the loss of their hierarchical structure information. Meanwhile, traditional neural networks cannot predict many Out-of-Vocabulary (OoV) words in the terminal node values of AST. To alleviate the above problem, in this paper, we propose a Multiple-Path Learning neural network model for code completion (MPL) based on an AST by learning from a large-scale corpus. In this model, multiple paths such as context path, root path, and terminal node path are established to understand different code features required for node prediction and improve code representation ability. Based on the principle of program local repeatability, it also adopts a replication mechanism to copy the appropriate OoV words from the local terminal node path as the prediction result, further improving the prediction accuracy. The experimental results show that the MPL model has better performance than existing methods on the code completion task.

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