Enabling Better QoS in Fog Computing through Adaptive Clustering and Load Balancing Strategy

Abstract

Quality of Service standards have been upheld by the researchers using several methods. Major thing in the cloud traffic is the job scheduling, which is disrupted due to the increased service delay brought on by the heavy traffic. This results in an imbalanced strain on the fog environment and degrade the network performance. The suggested approach makes use of a novel model which deals with the distribution of the requests by the fog nodes i.e., load, and do the task scheduling on the cloud-based servers. In the work, authors have gone through the various existing algorithms and the implemented models for the task scheduling, and how the task scheduling is impactful in the load balancing of the fog nodes requests. The proposed system is a novel optimal load balancing method in the cloud which inspired from the various existing optimization techniques. The proposed work is compared with the existing models, and performed better in terms of Delay analysis, and the load balancing parameters like average waiting time, task carrying time and average task completion time.