Quasi-Sobol-Based Optimization and Sigmoid Fuzzy Logic for Efficient Task Clustering in Decentralized Edge-Cloud Architectures

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nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]20/09/2025 at 3:50 PM[/if 2224] | [if 1553 equals=””] Volume : 12 [else] Volume : 12[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02 | Page : 9 18

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    Dinesh Kumar Reddy Basani, Rajya Lakshmi Gudivaka, Sri Harsha Grandhi, Basava Ramanjaneyulu Gudivaka, Raj Kumar Gudivaka5, M.M. Kamruzzaman,

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  1. Engineer, Engineer, Engineer, Engineer, Engineer, Assistant Professor, CGI, Department of Computer Science, British Columbia, Wipro, Department of Computer Science, Intel, Folsom, Department of Computer Science, Department of Computer Science, Raas Infotek, Department of Computer Science, Surge Technology Solutions Inc, Department of Computer Science, College of Computer and Information Sciences Jouf University, Hyderabad, California, Delaware, Texas, Sakakah, Canada, India, USA, USA, USA, Saudi Arabia

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Abstract

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nDecentralized edge-cloud systems face a lot of issues in efficient task clustering, resource allocation, and real-time decision-making. Conventional methods mostly fail to work well under dynamic workloads and uncertain conditions. This study intends to optimize task clustering through quasi-Sobol-based optimization and Sigmoid Fuzzy Logic for resource allocation, enhancing decision-making accuracy and achieving efficiency in the system at the edge-cloud environment. A hybrid technique that has incorporated optimization via Quasi-Sobol sequences along with Sigmoid Fuzzy Logic for the decision-making block has been developed. The developed approach was benchmarked against prevailing techniques and then judged regarding accuracy enhancement, precision boost, and bettering system performance. Accuracy, precision, recall, and F1-Score significantly improved, proving its efficiency in real-time task clustering and resource allocation within decentralized systems. The new method optimizes the performance of the decentralized edge-cloud environment by improving task clustering and resource allocation and provides a real-time, scalable solution for large-scale applications.nn

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Keywords: Task clustering, edge-cloud, Quasi-Sobol optimization, sigmoid fuzzy logic, resource allocation, efficiency

n[if 424 equals=”Regular Issue”][This article belongs to Trends in Machine design ]

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How to cite this article:
nDinesh Kumar Reddy Basani, Rajya Lakshmi Gudivaka, Sri Harsha Grandhi, Basava Ramanjaneyulu Gudivaka, Raj Kumar Gudivaka5, M.M. Kamruzzaman. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Quasi-Sobol-Based Optimization and Sigmoid Fuzzy Logic for Efficient Task Clustering in Decentralized Edge-Cloud Architectures[/if 2584]. Trends in Machine design. 10/06/2025; 12(02):9-18.

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How to cite this URL:
nDinesh Kumar Reddy Basani, Rajya Lakshmi Gudivaka, Sri Harsha Grandhi, Basava Ramanjaneyulu Gudivaka, Raj Kumar Gudivaka5, M.M. Kamruzzaman. [if 2584 equals=”][226 striphtml=1][else]Quasi-Sobol-Based Optimization and Sigmoid Fuzzy Logic for Efficient Task Clustering in Decentralized Edge-Cloud Architectures[/if 2584]. Trends in Machine design. 10/06/2025; 12(02):9-18. Available from: https://journals.stmjournals.com/tmd/article=10/06/2025/view=0

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Volume 12
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received 15/04/2025
Accepted 25/04/2025
Published 10/06/2025
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Publication Time 56 Days

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