Lower and Upper Quartiles Enhanced Round Robin Algorithm for Scheduling of Outlier Tasks in Cloud Computing

Abstract

Cloud computing is one of the top emerging technologies with huge market and enterprise potential as it provides on-demand, -based access to large-scale shared computing resources. Task scheduling is one of the most important issues in cloud computing in order to enhance performance and resource utilization while minimizing costs. Because of its simplicity and fairness, the round-robin algorithm is the ideal task scheduling algorithm, although it suffers from time complexity and cannot handle outlier tasks. Several modifications of Round Robin have been introduced to enhance time complexity. To ensure sufficient deal with time complexity and outlier tasks, this paper introduces a novel enhanced round-robin heuristic algorithm by utilizing the round-robin algorithm and updating its time quantum dynamically based on the lower and upper quartiles of the time quantum for all the tasks in the ready queue. The experimental results on four datasets showed that the proposed algorithm significantly outperformed baseline algorithms in terms of the average waiting time, turnaround time, and response time. The results show that, when compared to the baseline algorithm in cases 3 and 4, the proposed algorithm enhances the average waiting time's time complexity by 50% with datasets containing random and outlier tasks.