Dynamic task allocation in fog computing using enhanced fuzzy logic approaches

Fog computing extends cloud services to the edge of the network, enabling low-latency processing and improved resource utilization, which are crucial for real-time Internet of Things (IoT) applications. However, efficient task allocation remains a significant challenge due to the dynamic and heterogeneous nature of fog environments. Traditional task scheduling methods often fail to manage uncertainty in task requirements and resource availability, leading to suboptimal performance. In this paper, we propose a novel approach, DTA-FLE (Dynamic Task Allocation in Fog computing using a Fuzzy Logic Enhanced approach), which leverages fuzzy logic to handle the inherent uncertainty in task scheduling. Our method dynamically adapts to changing network conditions, optimizing task allocation to improve efficiency, reduce latency, and enhance overall system performance. Unlike conventional approaches, DTA-FLE introduces a novel hierarchical scheduling mechanism that dynamically adapts to real-time network conditions using fuzzy logic, ensuring optimal task allocation and improved system responsiveness. Through simulations using the iFogSim framework, we demonstrate that DTA-FLE outperforms conventional techniques in terms of execution time, resource utilization, and responsiveness, making it particularly suitable for real-time IoT applications within hierarchical fog-cloud architectures.