Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing

Area of Science:

• Computer Science
• Distributed Computing
• Network Engineering

Background:

• Cloud computing faces challenges with server proximity, causing service delays and reduced user satisfaction.
• Fog computing emerges as a distributed architecture extending cloud capabilities to address proximity issues.
• Efficient resource scheduling is crucial for fog computing performance.

Purpose of the Study:

• To propose an optimized resource scheduling method for fog computing environments.
• To enhance user satisfaction and operational efficiency in fog computing systems.

Main Methods:

• Standardization and normalization of resource attributes.
• Integration of fuzzy clustering with particle swarm optimization for resource division.
• Development of a novel resource scheduling algorithm based on optimized fuzzy clustering.

Main Results:

• The proposed method effectively reduces the search space for resource allocation.
• Experimental results demonstrate significant improvements in user satisfaction.
• The efficiency of resource scheduling in fog computing is notably enhanced.

Conclusions:

• The optimized fuzzy clustering-based resource scheduling algorithm is effective for fog computing.
• This approach addresses key challenges in distributed computing, improving service delivery.
• The findings contribute to the advancement of efficient fog computing resource management.