Algorithms for Task Allocation in Fog Computing
Anagha Jadhav, S. Mini · 2024
Fog computing is a decentralized infrastructure that brings computing, storage, and networking resources closer to data sources and end devices, significantly reducing latency and enhancing performance for delay-sensitive applications such as real-time video streaming and self-driving cars. A critical challenge in fog computing is task scheduling, which involves assigning tasks to fog nodes to minimize latency and optimize performance metrics. This paper reviews existing algorithms for task scheduling and resource allocation in fog computing and introduces a approach that compares performance of Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Multi-Agent Systems (MAS), and the Firefly Algorithm implemented in proposed framework. The proposed framework aims to enhance system efficiency by optimizing task allocation among helper nodes. Each algorithm's effectiveness is evaluated in achieving balanced task allocation within the fog network environment. Experimental results demonstrate that the PSO integrated algorithm consistently outperforms other algorithms in terms of task distribution efficiency, achieving better latency reduction and resource utilization.