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检索条件"任意字段=2nd International Conference on Parallel, Distributed, Grid and Cloud Computing for Engineering"

共 1401 条记录，以下是281-290 订阅

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Research on multi-chain blockchain consensus mechanism model under cloud computing platform

Research on multi-chain blockchain consensus mechanism model...

引用

Electrical, Automation and Computer engineering (ICEACE), international conference on

作者： Yuan Li Modern Finance Industry School Shandong Institute of Commerce and Technology Jinan China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368215

This paper combines the advantages of cloud computing platform in resource scheduling, proposes a consensus mechanism model for multi-chain blockchain, and designs corresponding algorithms, aiming to optimize the overall performance of the system and the efficiency of cross-chain communication. The model achieves efficient communication and load balancing between chains by introducing the distributed resource scheduling scheme of cloud computing. In terms of consensus mechanism, this paper uses a dynamic adjustment strategy based on cloud computing resources to improve the throughput and consensus speed of the system. This paper conducts detailed simulation tests to verify the effectiveness of the model. The test results show that under different loads, the system shows significant improvements in consensus delay, resource utilization and cross-chain communication success rate. Especially under high load, the system throughput is increased by about 20%, the consensus delay is reduced by 15%, and the cross-chain communication success rate is increased by 10%. These data show that the model proposed in this paper can effectively use the resources of the cloud computing platform to provide more flexible resource scheduling and cross-chain communication support while improving the performance of the multi-chain blockchain system.

关键词： cloud computing Processor scheduling Computational modeling Simulation Throughput Dynamic scheduling Consensus protocol Delays Resource management Load modeling

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A Comparison of cloud Security Mechanisms

A Comparison of Cloud Security Mechanisms

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Automation, computing and Renewable Systems (ICACRS), international conference on

作者： D. Saravanan M. Maragatharajan E. Nirmala P. Naveen A Francis Saviour Devaraj School of Computing Science and Engineering VIT Bhopal University Kothrikalan Madhya Pradesh India Department of Electrical and Electronics Engnieering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu India Department of IT College of Computing & Information Sciences University of Technology and Applied Sciences Ibri Sultanate of Oman

cloud computing is a broad term that can be understood differently depending on the context. Its primary goal is to reduce capital expenses on infrastructure, operating systems, deployment, and storage. The National Institute of Standards and Technology (NIS T) defines cloud computing as on-demand network access to a shared pool of computing resources that can be rapidly provisioned and released with minimal management effort or service provider interaction. cloud computing operates using virtualization and parallel computing principles and provides services like IaaS, PaaS, SaaS, and NaaS. The cloud is classified into public, private, hybrid, and community clouds, each with its own unique features. The major issue of cloud computing is to transfer the data with security breaches. Various security systems are available to protect the data security in the cloud. This research study has discussed some security models like Advanced encryption standard algorithm, Biometric model, Elliptic-curve cryptography with Diffie-Hellman algorithm and a new technique vector-matrix based encryption algorithm. The execution time for these algorithms were compared and results were discussed.

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The parallel DCNN-LSTM Model for cloud computing Prediction Based on an Improved SG Filter

The Parallel DCNN-LSTM Model for Cloud Computing Prediction ...

引用

Image Processing and Computer Applications (ICIPCA), IEEE international conference on

作者： Bowen Yang Kaiming Jin Longhua Zou School of Computer Science and Engineering Beihang University Beijing China School of Economics Xiamen University Xiamen China

ISBN: (数字)9798350360240

ISBN: (纸本)9798350384161

It is crucial to accurately predict the workload of large-scale cloud computing resources in order to better allocate and utilize them, but this has always been seen as a challenging task. Recently, LSTM has been widely used in the field of time series prediction, but it is not sufficient to learn complex and noisy patterns. This work aims to solve the accuracy problem of cloud data prediction and proposes a parallel dilated convolutional network. Firstly, in order to smooth and remove noise from the data, an improved SG filter that avoids future data leakage is proposed. Afterwards, this work combines LSTM with dilated convolution and adopts a parallel architecture. The purpose is to enable the model to learn patterns and potential representations of data from multiple scales, and enhance model's generalization and robustness. Finally, this work validated the performance of the proposed model on real datasets, Google Cluster Trace. Experimental results have shown that the proposed model performs better than LSTM and some widely used variants.

关键词： cloud computing Computational modeling Time series analysis Predictive models Feature extraction Data models Robustness parallel architectures Convolutional neural networks Long short term memory

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Intelligent cloud Data Indexing and Retrieval Through Random Forest Algorithms

Intelligent Cloud Data Indexing and Retrieval Through Random...

引用

international conference on Signal Processing, Communication, Power and Embedded System (SCOPES)

作者： K. Mohan Raj A. Karthikeyan Department of CSE. Mahendra Engineering College Namakkal Tamil Nadu India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Another significant research issue is that of data indexing and retrieval in cloud computing context given that the virtues of velocity, variety, and volume are escalating pervasively. Indexing techniques have in the past proved difficult for real-time cloud applications requirements. This research focuses on the utilization of developed Random Forest algorithms as a new conception of smart cloud data indexing and search. The reported method exploits the strict parallelism and scaling properties of Random Forest algorithms to design a distributed and robust indexing scheme that can efficiently process lager cloud data. During the indexing phase, feature extraction takes place followed by partitioning of the data, construction of several decision trees for search capability. For data retrieval experiments performed on benchmark cloud data sets, the results indicate that the proposed Random Forest-based indexing achieves average accuracy equal to 92.4%, the efficiency is 15-20% higher than traditional indexing methods. The method also stands high on parameters of data skew and node failure for providing reliable performance with frequent change in cloud-based infrastructure. The introduction of Random Forest algorithms into cloud data indexing serves as a boiling prospect in creating scalability and efficiency in the discovery process amidst the growing big data problem. The developments proposed within this research point to the future developments regarding intelligent cloud data management and create new opportunities for this topic within the field of cloud computing.

关键词： cloud computing Scalability Signal processing algorithms Signal processing parallel processing Real-time systems Partitioning algorithms Reliability Random forests Indexing

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Empirical cloud computing Remote Human Resource Work Efficiency System Based on Data Mining

Empirical Cloud Computing Remote Human Resource Work Efficie...

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Sensors, Electronics and Computer engineering (ICSECE), IEEE international conference on

作者： Linfang Wu Guangzhou Huashang Vocational College Guangzhou China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

In order to improve the dynamic monitoring ability of power grid human resource management system, fuzzy information cluster analysis technology is used to study the dynamic monitoring technology of power grid human resources. This paper establishes a modern HR management system based on cloud computing. The virtual architecture layer mainly collects entities and virtual computing resources in multiple Spaces, and integrates these resources into a dynamically adjustable database to realize the physical storage and transmission of data. The row and column decisions of the situation are obtained through multiple objective situation decisions. Using multiple target situation decision matrix, the optimal situation is selected for personnel allocation and fed back to the display layer. The distribution structure model of power consumption human resources is established and the distribution cluster center of power consumption human resources is obtained. In this paper, the multi-dimensional time scale features of personnel data are examined by clustering method, and the automatic identification and fuzzy extraction of personnel data are realized. The state equation of the model is obtained by simulating the model. The accuracy and rapidity of this algorithm in human resource data monitoring are verified by statistical analysis of power grid human resource data. The research results of this paper provide an effective means for the effective utilization of power grid human resources.

关键词： Power demand Power system dynamics Feature extraction Dynamic scheduling Power grids Mathematical models Personnel Resource management Data mining Monitoring

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Research on network performance optimization technology based on cloud-edge collaborative architecture 2

Research on network performance optimization technology base...

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2nd IEEE international conference on Big Data, Artificial Intelligence and Internet of Things engineering, ICBAIE 2021

作者： Ou, Qinghai Wang, Yanru Song, Wei Zhang, Ningchi Zhang, Jie Liu, Hui Beijing Fibrlink Communications Co. Ltd. Beijing China State Grid Jibei Information Telecommunication Company Beijing China Beijing Fibrlink Communications Co. Ltd. Communication Technology Center Beijing China

ISBN: (纸本)9780738131221

In order to satisfy the needs of smart grid terminal access and differentiated services, it is necessary to accelerate the integration and development of the Internet of Things and smart grids. cloud computing and edge computing are the key to promoting the development of the Internet of Things. The synergistic integration of cloud computing and edge computing can shape the power of the Internet of Things. future. Therefore, this paper proposes a cloud-edge collaboration architecture based on the cloud-edge collaboration architecture by studying the cloud-edge collaboration method of the power Internet of Things, and discusses the deployment mode of cloud and edge IoT agent collaboration based on the cloud-edge collaboration architecture, and proposes a network performance optimization deployment plan under cloud-edge collaboration. This solution can realize the reasonable allocation of resources and optimize network performance under the cloud-edge collaborative architecture. © 2021 IEEE.

关键词： Edge computing

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FSTOR: A distributed Storage System that Supports Chinese Software and Hardware

FSTOR: A Distributed Storage System that Supports Chinese So...

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international conference on Wireless Communications, Networking and Applications, WCNA 2021

作者： Lin, Yuheng Wang, Zhiqiang Zhao, Jinyang Chen, Ying Chi, Yaping Cyberspace Security Department Beijing Electronic Science and Technology Institute Beijing China Department of Cryptography and Technology Beijing Electronic Science and Technology Institute Beijing China Beijing Baidu T2Cloud Technology Co. Ltd. 15A#-2nd Floor En ji xi yuan Haidian District Beijing China

ISBN: (纸本)9789811924552

In order to develop a distributed storage system that adapts to Chinese software and hardware, build a cloud computing platform that is independently usable, safe and reliable, data utilization is more concentrated and intelligent, and service integration is more unified and efficient. This paper designed and implemented a distributed storage system that supports Chinese software and hardware, which is compatible with Chinese mainstream CPU, operating system, database, middleware and other software and hardware environments. After a lot of experiments and tests, it is confirmed that the system has high availability and high reliability. © 2022, The Author(s).

关键词： Middleware

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The Application of cloud computing and Support Vector Machine in the Construction of Entrepreneurial Model

The Application of Cloud Computing and Support Vector Machin...

引用

Sensors, Electronics and Computer engineering (ICSECE), IEEE international conference on

作者： Yali Xiong School of Management Shanghai University Shanghai China

ISBN: (数字)9798350373646

ISBN: (纸本)9798350373653

This paper studies the resource integration service of enterprise innovation team. This paper constructs a parallel support vector machine model based on multi-layer SVM, and develops an application system based on cloud platform innovation and entrepreneurship services. Then this paper studies the time series minimum optimization method based on large-scale data. The MapReduce technique is used to partition the massive training set and construct multi-layer support vector machine. Samples are collected around the optimal surface of each support vector machine. Experiments show that the proposed method is much faster in both time and recognition accuracy than the single or conventional parallel search methods. All the unsuccessful “sub-satisfactory” personnel will conduct the next round of “seeking talents”. After several rounds of screening, a fixed entrepreneurial team match was formed. This algorithm is the “delay receive” operation rule, which makes the algorithm more fast, practical and stable. It can effectively improve the resource allocation effect of the system.

关键词： Support vector machines Training Technological innovation cloud computing Time series analysis Entrepreneurship Vectors Stability analysis Delays Resource management

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The active disturbance rejection control for the self-balancing moving platform of cable driven parallel robots 2

The active disturbance rejection control for the self-balanc...

引用

2nd international conference on Artificial Intelligence, Information Processing and cloud computing, AIIPCC 2021

作者： Xiao, Di Zhang, Shangying Xiang, Bao School of Mechanical and Electrical Engineering Wuhan Institute of Technology Wuhan430205 China Wuhan Yuqiong Technology Co. Ltd. Wuhan430205 China

ISBN: (纸本)9783800756162

A high precision control scheme based on active disturbance rejection control (ADRC) is proposed to improve the stability accuracy of the moving platform of 3DOF cable drive parallel robot. The ADRC can actively extract disturbance signals from the input signal of the controlled system, so as to suppress the influence of the disturbance on the moving platform. The ADRC based on current loop is designed, the internal and external multisource disturbances of the system can be estimated into the total interference in real time by the extended state observer (ESO). The disturbance rejection ability of the ADRC is analyzed by simulations, the simulation results show that the ADRC has excellent capability in disturbance rejection by comparing with the conventional PID controller and ESO/PID controller. It is found that when the frequency is below 5Hz, the greater is the vibration frequency of the stabilized platform, the stronger is the disturbance rejection ability of ADRC. © 2021 AIIPCC 2021 - 2nd international conference on Artificial Intelligence, Information Processing and cloud computing. All rights reserved.

关键词： Disturbance rejection

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Real time forward solution of redundantly constrained cable driven parallel robots 2

Real time forward solution of redundantly constrained cable ...

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2nd international conference on Artificial Intelligence, Information Processing and cloud computing, AIIPCC 2021

作者： Xiang, Bao Zhang, Shangying Xiao, Di School of Mechanical and Electrical Engineering Wuhan Institute of Technology Wuhan430205 China Wuhan Yuqiong Technology Co. Ltd. Wuhan430205 China

ISBN: (纸本)9783800756162

The Levenberg-Marquardt and Quasi-Newtonian synthesis algorithm is presented by solving the forward position solution problem. The kinematics model of 8 Cable-6 DOF cable-driven parallel robots is established, and its mechanism is parameterized to solve the inverse solution problem, the inverse solution problem is calculated and solved. According to the closed principle of vectors, the nonlinear forward solution equations are derived from the inverse solution equation. The initial pose of the moving platform is taken as the initial value, and the forward position solution is obtained iteratively by using the Levenberg-Marquardt and Quasi-Newtonian synthesis algorithm. The circular curve is taken as the trajectory of the moving platform, and the real-time simulation results show that the accurate forward solution of position and pose can be completed by this algorithm. The experimental results show that the average time of the five pose combinations is less than 0.08ms, which meets the real-time requirements of solving the forward position and attitude solution of the cable-driven parallel robot. © 2021 AIIPCC 2021 - 2nd international conference on Artificial Intelligence, Information Processing and cloud computing. All rights reserved.

关键词： Cables

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