Design and implementation of the distributed system using an orchestrator based on the data flow paradigm

Kubiuk, Yevhenii; Kharchenko, Kostiantyn

doi:10.15587/2706-5448.2020.205151

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…The purpose of the general data storage service is to allow applications to classify non-database data such as pictures, text, and compressed packages uploaded by users through SVM, and then store them through this service, so that these contents can be managed uniformly and reduce campus file data loss. The danger and migration difficulty [18][19].…”

Section: General Data Storage Service Based On Svmmentioning

confidence: 99%

Campus Distributed System Platform Considering Support Vector Machine Algorithm

2021

DPS

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With the increase of enrollment scale, the traditional management model of the school has brought huge pressure, and various business processes are flooded with all aspects of education and teaching management. In order to solve these serious problems, the introduction of information technology and the use of network technology, Web technology, to standardize various business processes has become an imperative task. Distributed processing technology decouples and splits the business content in the original single software system. According to business requirements, these split subtasks are distributed to processing nodes with different business processing capabilities for processing, realizing the task processing. Efficiency, perfect for handling campus business. Therefore, this paper designs a distributed system(DS) for the business management of the campus, builds a network topology map through the campus network, and then classifies the campus data according to the characteristics of the support vector machine algorithm classification to achieve a unified and orderly information management. In this paper, the data collection efficiency of the DS is tested under different nodes, and the results show that with the multiplication of collection nodes, the average number of web pages per second is multiplied, and the total time consumption is multiplied.

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Section: General Data Storage Service Based On Svmmentioning

confidence: 99%

Campus Distributed System Platform Considering Support Vector Machine Algorithm

2021

DPS

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“…The methods, methods and algorithms of traditional distributed and parallel systems are not suitable for task assignment in IoT systems due to their own characteristics. Reinforcement learning methods allow you to solve the problem of building distributed systems due to the adaptive formation of sequences of computing nodes and corresponding computing tasks [2]. Khan NA proposes an optimal homotopy analysis algorithm using nonlinear reaction-diffusion systems.…”

Section: Introductionmentioning

confidence: 99%

Integration Technology of Distributed System Based on Multi-objective Hotopy Algorithm

2022

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With the progress and growth of society, the field of computer science and technology has also continued to improve rapidly. Human beings have conducted in-depth research on algebraic learning, solving multi-objective homotopy algorithm nonlinear equations and applying them to integrated technology systems of distributed systems. More and more people's attention. In order to solve the shortcomings of the existing research on the integration technology of the distributed system of the multi-objective homotopy algorithm, this paper uses the function equation and the integration of the distributed system in the distributed system integration technology based on the function of the multi-objective homotopy algorithm. On the basis of discussing the characteristics of the technology, the development tools and development environment of the distributed system integration technology based on the multi-objective homotopy algorithm are briefly introduced. And it discusses the workflow design of the model of the integration technology of the distributed system based on the multi-objective homotopy algorithm, and finally compares the data classification accuracy and operation of the application of the multi-objective homotopy algorithm in the integration technology of the distributed system with CGX, The SVN algorithm is compared and experimentally analyzed. The experimental data shows that the multi-objective homotopy algorithm is better than the CGX and SVN algorithms in data classification accuracy, and the classification accuracy is as high as 99.87%. In the running situation, the multi-objective homotopy algorithm and CGX are not much different.but the multi-objective homotopy algorithm is still better than CGX.

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“…is the input vector [11]. In a compact set  ,The accuracy of RBNN can be derived from all linear and nonlinear function regions [12]. Given a smooth nonlinear vector function, The data layer neural network can continuously approach the nonlinear quantitative function…”

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confidence: 99%

Adaptive Multi-layer Distributed System Based on Neural Network

2022

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with the popularization and development of computer and network information technology, network information and the Internet communication of all kinds of information is distributed in all areas of the network, which makes the multi-tier distribution is the development of the system from the host system for the server application, multilayer distributed system has gradually become the mainstream of computer and network information technology. In order to solve the problems in the multi-layer distributed system based on neural network adaptation, this paper first introduces the basic structure of the multi-layer distributed system and the steps of the approximation function of RBF neural network adaptation, and then briefly describes the development tools and software Settings of the multi-layer distributed system based on neural network adaptation. Finally, the architecture of neural network adaptive multi-layer distributed system is designed and discussed. Finally, the application of neural network adaptive and other algorithms in multi-layer distributed system is compared with experimental data. The experimental results show that the multilayer distributed system based on neural network adaptive in the application of noise signal to the noise cancellation effect of sawtooth wave and output letter manic ratio up to 26.23, 26.02 in the rectangular wave, 24.79 in the sine wave, so it can be seen that the multilayer distributed system based on neural network adaptive of superiority.

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Design and implementation of the distributed system using an orchestrator based on the data flow paradigm

Cited by 4 publications

References 5 publications

Campus Distributed System Platform Considering Support Vector Machine Algorithm

Campus Distributed System Platform Considering Support Vector Machine Algorithm

Integration Technology of Distributed System Based on Multi-objective Hotopy Algorithm

Adaptive Multi-layer Distributed System Based on Neural Network

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