Design of load-aware resource allocation for heterogeneous fog computing systems.

Area of Science:

• Computer Science
• Distributed Systems
• Network Engineering

Background:

• Cloud computing offers centralized services, while fog computing provides distributed resources near end devices.
• Fog computing is suitable for large-scale applications, reducing delay and network load compared to cloud architectures.
• Efficient resource distribution and load balancing are critical for effective system deployment.

Purpose of the Study:

• To propose a heuristic-based approach for optimizing fog computing resource utilization.
• To reduce network consumption and application delays in fog computing environments.
• To enhance the performance of delay-aware applications through efficient resource allocation.

Main Methods:

• Developed a heuristic algorithm for fog resource allocation.
• The algorithm considers data volume generated by edge node clusters.
• Resource allocation is dynamically adjusted based on edge data load.

Main Results:

• The proposed heuristic approach effectively reduces network consumption.
• Significant reductions in application delays were observed.
• Evaluations confirmed the approach's efficacy across various scales, achieving optimal performance.

Conclusions:

• The heuristic-based fog computing approach optimizes resource utilization.
• This method enhances efficiency for delay-aware applications by minimizing network load and latency.
• The findings support the scalability and effectiveness of the proposed strategy for distributed computing environments.