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Hybrid fuzzy clustering with Golden Eagle Optimization Algorithm for fault tolerant load balancing in fog computing
Harpreet Kaur1, Swati Malik2, Vidhu Baggan3
1Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
Scientific Reports
|August 7, 2026
Summary
This study introduces a Hybrid Fuzzy Clustering with Golden Eagle Optimization Algorithm (HFC-GEOA) for Fog Computing (FC) task scheduling. HFC-GEOA enhances performance by considering virtual machine (VM) health, significantly reducing task turnaround and wait times while improving reliability.
Area of Science:
- Computer Science
- Cloud Computing
- Network Engineering
Background:
- Fog Computing (FC) integrated with 5G offers reduced latency and enhanced Quality of Service (QoS).
- Existing scheduling strategies often neglect virtual machine (VM) health indicators (CPU, memory, reliability), compromising fault tolerance and load balancing.
- Task assignment in FC systems requires robust mechanisms to handle resource constraints and ensure system stability.
Purpose of the Study:
- To propose a novel scheduling framework, Hybrid Fuzzy Clustering with Golden Eagle Optimization Algorithm (HFC-GEOA), for Fog Computing environments.
- To address the limitations of current schedulers by incorporating VM health indicators and adaptive load balancing.
- To evaluate the scalability, performance, and reliability of the proposed HFC-GEOA framework in heterogeneous FC systems.
Main Methods:
- Developed a three-level capacity-aware VM clustering approach (HCC, MCC, LCC).
- Implemented fuzzy-based task prioritization and localized metaheuristic optimization using the Golden Eagle Optimization Algorithm (GEOA).
- Utilized iFogSim2 for extensive simulations across various task-VM scenarios (1,000-20,000 tasks; 20-1,500 VMs) with 30 independent runs.
Main Results:
- HFC-GEOA significantly reduced average turnaround time (up to 78%) and average wait time (up to 91%) compared to existing methods (FGELB, ACO-LWC, FCLB).
- Achieved competitive energy consumption, 11-16% less than ACO-LWC.
- Maintained stable system reliability (0.71-0.74), more than double that of EWOA, with controlled failure rates (2.88-3.81%).
- Demonstrated improved fault tolerance scores and enhanced system resilience at scale.
Conclusions:
- HFC-GEOA provides a scalable, health-conscious, and fault-tolerant scheduling solution for heterogeneous Fog Computing systems.
- The framework effectively balances latency, energy usage, and reliability, outperforming existing approaches in large-scale scenarios.
- The integration of VM health indicators and advanced optimization techniques is crucial for efficient FC resource management.
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