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Container-Aware Service Function Chains Placement and Optimization in Vehicular Edge computing 22

Container-Aware Service Function Chains Placement and Optimi...

引用

22nd IEEE international symposium on Parallel and distributed Processing with applications, ISPA 2024

作者： Zhang, Kaixin Hu, Shihong Qu, Zhihao Ye, Baoliu Hohai University School of Computer Science and Software Engineering China Nanjing University National Key Laboratory for Novel Software Technology China

ISBN: (纸本)9798331509712

In the domain of Vehicular Edge computing (VEC), this paper addresses the complex problem of Service Function Chain (SFC) placement, which is crucial for the efficient deployment of cloud applications in vehicular environments. Our approach leverages Virtual Network Function (VNF) technology, transitioning network services from traditional hardware to more flexible, container-based edge computing frameworks. The primary objective is to organize these VNFs into functional SFCs, thereby minimizing service delays in VEC systems. SFC placement faces two significant challenges. The first is the sequential dependency among VNFs within an SFC, which adds substantial complexity to container deployment. The second challenge is the cold start delay of VNF containers, a critical issue in scenarios requiring swift response times, which negatively impacts service quality in VEC applications. To address these challenges, we propose a comprehensive model for SFC placement that considers the deployment status of VNF containers, acknowledging the complexity of this NP-hard problem. The core of our contribution is the development of the Single SFC Placement Algorithm (SSPA), a sophisticated, greedy-based approach designed for the effective placement of individual SFCs. We enhance this algorithm by incorporating a Particle Swarm Optimization (PSO) technique, making it capable of efficiently handling the placement of multiple SFCs. Our solution aims to improve edge resource utilization and mitigate startup delays associated with the initial activation of containers, thereby reducing service delays. Extensive experimental evaluations demonstrate that our algorithm achieves a 34.3% average reduction in service delay compared to four baselines and a 5.8% average reduction compared to other container-aware algorithms. © 2024 IEEE.

关键词： service function chain SFC placement vehicle edge computing virtual network function

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Ant: An Efficient Lossless Compression Algorithm for IoT Time Series Data 21

Ant: An Efficient Lossless Compression Algorithm for IoT Tim...

引用

21st IEEE international symposium on Parallel and distributed Processing with applications, 13th IEEE international Conference on Big Data and Cloud computing, 16th IEEE international Conference on Social computing and Networking and 13th international Conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Li, Junhui Xu, Guangping Yang, Hongzhang Wu, Yulei Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education China University of Bristol Faculty of Science and Engineering Bristol United Kingdom

ISBN: (纸本)9798350329223

In various applications in Internet of Things like industrial monitoring, large amounts of floating-point time series data are generated at an unprecedented rate. Efficient compression algorithms can effectively reduce the size of data, enhance transmission performance and storage efficiency, and simultaneously lower storage costs. Therefore, there is a need for lightweight and efficient stream compression algorithms. In this paper, we propose a novel lossless floating-point data compression algorithm called Ant. The main idea is to encode double-precision floating-point numbers into integer form, calculate the delta between adjacent values, and then convert the delta into unsigned integers. This encoding method effectively reduces storage costs and improves data compression efficiency. Extensive experiments on real-world datasets demonstrate that our algorithm achieves compression speeds at least as fast as state-of-the-art streaming methods, and a 63% relative improvement in average compression rate. © 2023 IEEE.

关键词： Storage efficiency

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An Improved Hybrid Method for Power System Reliability Assessment 21

An Improved Hybrid Method for Power System Reliability Asses...

引用

21st international symposium on distributed computing and applications for business engineering and science, DCABES 2022

作者： Qu, Liping Gao, Tailu Zhang, Jie Liu, Bin Cui, Wenchao School of Electrical Engineering and Information Beihua University Jilin China

ISBN: (纸本)9781665454629

In order to improve the sampling efficiency of the non-sequential Monte Carlo simulation method, an improved hybrid method combining the analytical method and the significant Latin hypercube sampling method is proposed based on the idea of state space partitioning. The method partitions the system state space based on the determination of the significant state subspace to avoid sampling the zero-fault state of the system. The reliability index of the significant state subspace is efficiently solved by the analytical method, and the remaining state subspace is sampled by the significant Latin hypercube sampling method. Finally, the correctness and efficiency of the proposed algorithm is verified by evaluating the reliability of the IEEE-RTS system. © 2022 IEEE.

关键词： Importance sampling

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Nonlinear Control of Crane Systems Based on Intelligence computing of Disturbance Observer Under Mismatched Disturbance 22

Nonlinear Control of Crane Systems Based on Intelligence Com...

引用

22nd IEEE international symposium on Parallel and distributed Processing with applications, ISPA 2024

作者： Wang, Tianlei Wang, Yiyan Xu, Meng Tian, Jiajie Zhang, Jingling Hong, Zhiyong Wuyi University School of Electronic and Information Engineering Jiangmen China Jiangmen Key Laboratory of Kejie Semiconductor Bonding Technology and Control System Jiangmen China Wuyi University School of Mechanical and Automation Engineering Jiangmen China Guangdong Kejie Technology Co. Ltd Jiangmen529020 China

ISBN: (纸本)9798331509712

A control method based on disturbance observer is proposed to address the positioning drift issue of the gantry crane under unmatched disturbances, aiming at precise positioning of the trolley and effective payload swing angle attenuation in a variable-length two-dimensional lifting system. The control scheme comprises a combination of a proportional-derivative controller and a disturbance observer. Firstly, the design of the coupling function in this paper is based on the observation of the natural characteristics of the dynamical model. It cleverly designs the coupling function, which not only helps to effectively reduce the swing angle but also prevents the problem of positioning drift under non-matching disturbances, subsequently, design a controller by integrating intelligent computing with control theory. Secondly, the designed disturbance observer observes and compensates for the system output to achieve disturbance suppression. Thirdly, a thorough stability analysis has been conducted to demonstrate that the proposed controller satisfies the desired conditions. Finally, simulation results illustrate that the pro-posed method success-fully addresses the limitations of existing methods and exhibits superior performance and robustness. © 2024 IEEE.

关键词： Gantry cranes

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A semi-supervised GCN-based community detection algorithm 22

A semi-supervised GCN-based community detection algorithm

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22nd IEEE international symposium on Parallel and distributed Processing with applications, ISPA 2024

作者： Shi, Shu-Han Shi, Lei-Lei Liu, Lu Han, Zi-Xuan Jiang, Liang Chen, Fu-Xiang Jiangsu University School of Computer Science and Communication Engineering China University of Exeter Department of Computer Science United Kingdom University of Science and Technology School of Ocean Jiangsu China University of Leicester School of Computing and Mathematical Sciences United Kingdom

ISBN: (纸本)9798331509712

Community detection reveals the unique characteristics and relationships of in-network members, differentiated from out-of-community members and plays a pivotal role in network analysis. In recent years, deep learning techniques have made great strides in the application of community detection, especially on label sampling models, which train graph convolutional networks by constructing a balanced training set through structural centre localization and neighbourhood node expansion. However, such algorithms are limited on centre selection in community structure. To address this, this paper introduces a novel community detection algorithm based on peak density adaptive iterative segmentation, or LDACN in short. First, labels are assigned by adaptively selecting the centre node of the community structure, which results in a more even distribution of labels in the network. Subsequently, more accurate community segmentation is achieved by taking advantage of GCN's combined ability in capturing the connectivity relationships between the nodes and their intrinsic characteristics. Experimental results on synthetic and real-world network datasets show that our algorithm improves the effectiveness of community segmentation compared to the state-of-the-art algorithms. © 2024 IEEE.

关键词： Deep learning

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Prediction of urban traffic congestion time by BPneural network 21

Prediction of urban traffic congestion time by BPneural netw...

引用

21st international symposium on distributed computing and applications for business engineering and science, DCABES 2022

作者： Haoran, Liu Suyan, Zhao Xin, Liu Jie, Li Shandong University of Science and Technology Qingdao266000 China

ISBN: (纸本)9781665454629

This paper studies the time prediction of traffic congestion, through the real-time speed of the car, The free flow speed, the morning and evening peaks, the number of weeks, the average speed of the car in this section are used as independent variables, and the time of traffic congestion is studied as a dependent variable. Through the MATLAB R2014a numerical simulation software for BP neural network operation, the neuron input layer is the real-time speed, free flow speed, morning and evening peak, week number, and average vehicle speed factor of the car after processing. The prediction result is good, which proves that the model is effective and reliable, and can estimate the time of the vehicle passing through the crowded road section. © 2022 IEEE.

关键词： Traffic congestion

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Privacy-Preserving Federated Learning through Clustered Sampling on Fine-Tuning distributed non-iid Large Language Models 21

Privacy-Preserving Federated Learning through Clustered Samp...

引用

作者： Yun, Sheng Bhuiyan, Zakirul Alam Sadi, Md. Taufiq Al Hasib Su, Shen Fordham University Department of Computer and Information Sciences New York10458 United States North South University Department of Electrical & Computer Engineering Dhaka Bangladesh Guangzhou University Cyberspace Institute of Advanced Technology Guangzhou China

ISBN: (纸本)9798350329223

Recently, Large Language Models (LLMs) have been a phenomenal trend in the Artificial intelligence field. However, training and fine-tuning can be challenging because of privacy concerns and limited computing resources. Federated Learning (FL) has emerged as a novel machine learning framework offering privacy protection. The challenges in applying FL to real-world applications include dealing with heterogeneous data, poor client updates, and client selection. This paper introduces Privacy-preserving Federated Learning through Clustered Sampling on LLMs (FCLM), a framework that clusters models by their distribution similarity. It helps the model group similar models to improve text data heterogeneity handling and privacy concerns in distributed machine-learning environments. The FCLM framework is implemented and evaluated using popular Language models and text data. The framework shows a robust performance over the heterogeneous text data, which can further extend to the use of more complex LLMs. © 2023 IEEE.

关键词： Privacy-preserving techniques

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Unmanned aerial vehicle trajectory planning based on enhanced sparrow search algorithm 21

Unmanned aerial vehicle trajectory planning based on enhance...

引用

21st international symposium on distributed computing and applications for business engineering and science, DCABES 2022

作者： Tang, Anru Liu, Yian Song, Hailing Jiangnan University School of Ai and Computer Science Wuxi China Naval Research Institute Beijing China

ISBN: (纸本)9781665454629

In order to solve the problems of the original sparrow search algorithm (SSA) in the unmanned aerial vehicle (UAV) trajectory planning, such as low optimization accuracy and slow convergence speed, an enhanced sparrow search algorithm (ESSA) was proposed. Firstly, Halton sequence was used to initialize the population to increase the diversity of the population and improve the subsequent search accuracy of the algorithm. Secondly, the quasi-reflection learning mechanism is introduced to improve the individual quality of the algorithm after each iteration, and improve the optimization accuracy and convergence speed of the algorithm. The improved algorithm is applied to the trajectory planning of the UAV, the results show that the flight cost of the UAV trajectory found by ESSA is lower and the convergence speed is faster. © 2022 IEEE.

关键词： Antennas

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Radar emitter signal recognition based on coordinated attention 21

Radar emitter signal recognition based on coordinated attent...

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21st international symposium on distributed computing and applications for business engineering and science, DCABES 2022

作者： Jiajun, Ding Yunyang, Yan Yian, Liu School of Artificil Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9781665454629

Aiming at the problem that complex radar emitter signals are difficult to be recognized at low signal-to-noise ratio, a method based on improved coordinate attention network is proposed. Firstly, the radar signal is converted into a two-dimensional time-frequency image to reflect the signal feature information. Then the time-frequency image preprocessing and denoising by convolutional neural network. Finally, the coordinated attention network is used for feature extraction, and then the classification of radar emitter source signals are realized. Experiments results show that the proposed method can validly improve the accuracy of radar signal recognition under the condition of low SNR. © 2022 IEEE.

关键词： Signal to noise ratio

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DRAS: A Scheduling Algorithm for Cloud-Based DAG with Deadline Constraints 21

DRAS: A Scheduling Algorithm for Cloud-Based DAG with Deadli...

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作者： Zheng, Wei Deng, Jinglong Nan, Zihan Lin, Peixuan Hong, Yue Xiamen University Department of Computer Science Xiamen China Beijing Jiaotong University Department of Computer Science Beijing China

ISBN: (纸本)9798350329223

The scheduling problem of Directed Acyclic Graph (DAG) with deadline constraints in a cloud computing environment has not been thoroughly studied. This paper concentrates on maximizing resource utilization and scheduling success rate in such scenarios. To achieve this, a scheduling algorithm based on dynamic downward rank and average input duration(DRAS) is proposed. This algorithm builds upon an enhanced version of the list scheduling approach. The DRAS algorithm is compared with four other scheduling algorithms to evaluate its performance. The comparative analysis demonstrates that the DRAS algorithm outperforms the other algorithms in terms of resource utilization and scheduling success rate. © 2023 IEEE.

关键词： Scheduling algorithms

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