STM Journals Peer-reviewed research from Consortium e-Learning Network

Trends in Machine design Original Research

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

  1. Dinesh Kumar Reddy Basani CGI, Department of Computer Science, British Columbia
  2. Rajya Lakshmi Gudivaka Wipro, Department of Computer Science
  3. Sri Harsha Grandhi Intel, Folsom, Department of Computer Science
  4. Basava Ramanjaneyulu Gudivaka Department of Computer Science, Raas Infotek
  5. Raj Kumar Gudivaka5 Department of Computer Science, Surge Technology Solutions Inc
  6. M.M. Kamruzzaman Department of Computer Science, College of Computer and Information Sciences Jouf University

Abstract

Decentralized 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.

Keywords

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