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2nd International Conference on Automation, Computing and Renewable Systems, ICACRS 2023

作者： Rathinam, Anantha Raman Vathani, B. Santha Komathi, A. Lenin, J. Bharathi, B. Urugan, S.M. Department of Computer Science and Engineering - Honors KL University Guntur Andhra Pradesh Vijayawada India Department of Management Studies Mahalashmi Women’s College of Arts and Science Tamil Nadu Chennai India Department of Computer Science Nadar Saraswathi College of Arts and Science College Tamilnadu Theni India Department of ComputerScience and Engineering Alliance College of Engineering and Design Alliance University Karnataka Bengaluru India Department of Computer Science and Engineering R.M.K. Engineering College Tamil Nadu Chennai India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamil Nadu Chennai India

ISBN: (纸本)9798350340235

The use of sophisticated algorithms has radically altered cloud computing predictive auto-scaling and upkeep approaches. Recurrent Neural Networks (RNNs), the Prophet Algorithm, K-Means Clustering, and Seasonal Autoregressive Integrated Moving-Average (SARIMA) all play a role in improving cloud infrastructures, and their interactions are studied here. By capitalizing on their superiority in processing sequential data, RNNs can deduce accurate workload forecasts from past use patterns. Concurrently, the Prophet Algorithm records seasonal and annual patterns, which adds depth to forecasts. By grouping servers into clusters with similar consumption patterns, K-Means Clustering improves resource allocation efficiency and paves the way for precise auto-scaling. SARIMA models capture nuanced seasonal fluctuations, which lead to reliable demand forecasts. This explores the state-of-the-art and future directions of these techniques, illuminating their potential to revolutionize current approaches to cloud management. When these methods are combined, cloud service providers are better able to proactively scale their resources, hence reducing the likelihood of bottlenecks and outages. It foresees the development of these algorithms and their subsequent widespread use in a variety of fields outside of cloud computing, such as Internet of Things (IoT) networks and edge computing infrastructures. © 2023 IEEE.

关键词： Auto-Scaling Cloud Computing Data-driven Insights K-Means Clustering maintenance algorithms Predictive Analytics Prophet Algorithm Recurrent Neural Networks Scalability Seasonal Autoregressive Integrated Moving-Average

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A distributed agent-based approach to manage industrial wireless sensor networks

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IFAC-PapersOnLine 2015年第3期28卷 1439-1444页

作者： Taboun, M.S. Gholami, M. Breiman, R.W. Schulich School of Engineering University of Calgary CalgaryABT2N 1N4 Canada

Wireless sensor networks can be used to monitor many different applications, including factory automation. High-variety manufacturing is becoming the new norm, and requires systems capable of rapidly reconfiguring without downtime. Homogeneous wireless sensor networks may provide a system capable of minimal setup and change-over effort due to their hardware indifferences, but are constrained by limited node lifetime. In this research a distributed, a multi-agent cluster management system that attempts to regulate node death, encouraging maintenance approaches to reclaim and replace nodes is proposed. This system allows the network to continuously operate, and eliminates any downtime due to wireless node power loss. In this paper, an overview of a wireless sensor network management approach, the simulator platform developed using Java Agent Development Environment (JADE) that will be used to test this design, as well as preliminary experimental results using this simulator are proposed. The experimental results focus on the problem of tracking mobile nodes in an industrial wireless sensor network where wireless nodes are subject to battery depletion. The results of preliminary experiments with a heterogeneous network (i.e., a network with pre-defined cluster heads) show that a distributed, multi-agent systems approach is capable of adapting to disturbances resulting from node loss. © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Sensor nodes Computer software Factory automation Heterogeneous networks maintenance Manufacture Multi agent systems Wireless sensor networks Distributed agents Embedded information Industrial wireless sensor networks Java Agent Development Environment(JADE) maintenance algorithms maintenance approaches Manufacturing IS Wireless nodes

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