Performance analysis of workflow scheduling for load balancing approach based on QoS parameter in cloud platform

Cloud computing is the paramount model of high-performance computing. Because of its contingent and dynamic nature, demand for computing resources varies but remains finite. Cloud computing platforms are widely used for executing large-scale scientific workflows. However, efficient workflow scheduling remains a major challenge due to task dependencies, heterogeneous resources, and unequal workload distribution. These problems frequently lead to higher makespan and ineffective resource utilization. Resource management in cloud platforms is a key area of research that consists of two core components: scheduling dependent or independent tasks and provisioning resources for these tasks. Therefore, managing the ratio of demand and resources can be controlled by scheduling. It is a method for efficiently provisioning resources to users according to their demands, which can enhance system performance. Here, the demands are the user’s requests that can be considered as a task, and multiple tasks refer to the load that directly affects the processing capability of the system. This scheduling is also known as workflow scheduling, and it is depicted by a Directed Acyclic Graph (DAG) and is based on dependent tasks. This paper is based on the dependent task of DAG and contributes new approaches for workflow scheduling using graph shortest distance heuristics to achieve two major objectives: makespan and load balancing. This paper considers a random workflow and two scientific workflows: Epigenomics and Montage, for the assessment and evaluation of the proposed algorithm. Experimental results demonstrate that the proposed method consistently outperforms existing state-of-the-art scheduling algorithms in terms of reduced makespan by ranging from 49% to 55% for the Random workflow, 39% to 59% for the Montage workflow, and 19% to 49% for the Epigenomics workflow. It also improved load balance, ranging from 39% to 53% for Random workflow, 13% to 18% for Montage workflow, and 5% to 12% for Epigenomics workflow, along with the other QoS parameters across diverse system configurations. To validate the significance of performance improvements, statistical analysis was conducted using analysis of variance (ANOVA), along with effect size analysis, followed by Tukey’s honestly significant difference (HSD) post-hoc test. The statistical results confirm that the proposed approach achieves significantly lower makespan and improved load balance compared to state-of-the-art scheduling algorithms. This paper helps the researchers to explore workflow scheduling, designing, and issues in scheduling.