Two-level fuzzy-neural load distribution strategy in cloud-based web system

Zatwarnicki, Krzysztof

doi:10.1186/s13677-020-00179-6

Cited by 4 publications

(3 citation statements)

References 25 publications

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“…All systems presented in the solutions work in a way that minimizes the response time for each individual HTTP request. Our latest work [8,9,40,41] proposed a two-layer cloud architecture.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

“…This architecture was used while conducting the research in this article. Taking into account the results of the latest research [40,41], we propose the design of a new load balancer that distributes the HTTP request among web servers placed in one location (one region) in this article. Furthermore, several solutions for request response time prediction using artificial neural networks were tested and applied in Load Balancer.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

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Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

Pokuta

Zatwarnicki

2021

Applied Sciences

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Cloud computing systems revolutionized the Internet, and web systems in particular. Quality of service is the basis of resource configuration management in the cloud. Load balancing mechanisms are implemented in order to reduce costs and increase the quality of service. The usage of those methods with adaptive intelligent algorithms can deliver the highest quality of service. In this article, the method of load distribution using neural networks to estimate service times is presented. The discussed and conducted research and experiments include many approaches, among others, application of a single artificial neuron, different structures of the neural networks, and different inputs for the networks. The results of the experiments let us choose a solution that enables effective load distribution in the cloud. The best solution is also compared with other intelligent approaches and distribution methods often used in production systems.

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Section: Related Work and Motivationsmentioning

confidence: 99%

Section: Related Work and Motivationsmentioning

confidence: 99%

Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

Pokuta

Zatwarnicki

2021

Applied Sciences

Self Cite

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“…Fog computing is facility that uses edge devices to carry out fast streaming data analytics [5][6][7][8]. The characteristics of Fog computing are a) low latency, b) low data bandwidth, and c) sensor heterogeneity [9,10]. Cloud computing with big data analytics demands much expensive computing resources; therefore, downsizing the process being sent to cloud computing may be quite beneficial.…”

Section: Introductionmentioning

confidence: 99%

Fog Computing Application of Cyber-Physical Models of IoT Devices with Symbolic Approximation Algorithms

Choi

2021

Preprint

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Smart manufacturing systems transmit out streaming data from IoT devices to cloud computing; however, this could bring about several disadvantages such as high latency, immobility, and high bandwidth usage, etc. As for streaming data generated in many IoT devices, to avoid a long path from the devices to cloud computing, Fog computing has drawn in manufacturing recently much attention. This may allow IoT devices to utilize the closer resource without heavily depending on cloud computing. In this research, we set up a three-blade fan as IoT device used in manufacturing system with an accelerometer installed and analyzed the sensor data through cyber-physical models based on machine learning and streaming data analytics at Fog computing. Most of the previous studies on the similar subject are of pre-processed data open to public on the Internet, not with real-world data. Thus, studies using real-world sensor data are rarely found. A symbolic approximation algorithm is a combination of the dictionary-based algorithm of symbolic approximation algorithms and term-frequency inverse document frequency algorithm to approximate the time-series signal of sensors. We closely followed the Bayesian approach to clarify the whole procedure in a logical order. In order to monitor a fan's state in real time, we employed five different cyber-physical models, among which the symbolic approximation algorithm resulted in about 98% accuracy at a 95% confidence level with correctly classifying the current state of the fan. Furthermore, we have run statistical rigor tests on both experimental data and the simulation results through executing the post-hoc analysis. By implementing micro-intelligence with a trained cyber-physical model unto an individual IoT device through Fog computing we may alienate significant amount of load on cloud computing; thus, with saving cost on managing cloud computing facility. We would expect that this framework to be utilized for various IoT devices of smart manufacturing systems.

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Data Engineering Roadmap for Implementing Business Intelligence in Higher Education

Sequeira,

Reis,

Branco

et al. 2024

2024 IEEE 18th International Conference on Application of Information and Communication Technologies (AICT)

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Two-level fuzzy-neural load distribution strategy in cloud-based web system

Cited by 4 publications

References 25 publications

Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

Application of Neural Networks in Distribution of the Load in Cluster-Based Web Systems

Fog Computing Application of Cyber-Physical Models of IoT Devices with Symbolic Approximation Algorithms

Data Engineering Roadmap for Implementing Business Intelligence in Higher Education

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