Optimizing Fog Computing Performance: A Machine Learning-Based Task Scheduling Approach Using Fuzzy Logic and Clustering

The ability to deliver results promptly has become crucial for analytical frameworks due to the swift increase in data volumes. Complex algorithms are needed to schedule data processing activities on distributed compute clusters efficiently. Because it is impractical to create and fine-tune a scheduling policy for every task, current systems rely on basic, generic heuristics and neglect workload peculiarities. Effective task scheduling is essential for optimising the performance of fog computing systems. Fog nodes are dynamic, heterogeneous, and resource-constrained, and traditional task scheduling techniques, which were largely created for centralised cloud settings, frequently cannot accommodate these characteristics. We provide a cutting-edge machine learning (ML) based solution that adjusts job assignment to the evolving needs of the Fog environment in order to get beyond these restrictions. Fuzzy logic, a reliable unsupervised learning method, is used with the K-Means clustering algorithm in our method to effectively group. We have used Python to model and assess our suggested method. The simulation findings validate the effectiveness of our scheduling method, demonstrating notable improvements in response time, reduced network usage, and execution time reduction in comparison to other ML and non-ML based scheduling techniques available in the Python framework.