A Novel Hybrid Scheduling Approach for Enhancing Cloud System Performance

Several scheduling challenges exist during task computations, especially when algorithms lack intelligent mechanisms. Modern research confirms that Machine Learning (ML) algorithms enhance system performance through their intelligent mechanisms. This research used ML’s K-Means Clustering (KMC) algorithm along with the Shortest Job First (SJF) scheduling algorithm to design and implement a novel SJF-KMC hybrid scheduling approach to improve cloud system performance. This research experimented with Google Cluster real-time tasks in ten scenarios with varying Virtual Machine deployed from ten to one hundred and implemented the SJF-KMC approach across five clusters: SJF-K1, SJF-K3, SJF-K5, SJF-K7, and SJF-K9. The performance of these approaches was compared with each other and the existing SJF algorithm across Average Network Latency (Avg_NL), Average Energy (Avg_E), Average Memory (Avg_M), and Average Throughput (Avg_T). Results show that the task clustering provided by the SJF-KMC algorithms improves cloud performance. Additionally, results indicate that minimizing task clusters during their transit phase from the user environment to the cloud environment (and vice versa) improves Avg_NL, minimal/maximum task clusters during computations enhances Avg_E and Avg_M parameters, and maintaining a minimal number of clusters during Throughput calculations. The intelligent mechanism of the SJF-KMC approach enhances scheduling, thereby improving overall cloud system performance.