## 1 Introduction  
Many scientific tasks in the fields of bioinformatics, health care, physics, and astronomy have been observed to require the execution of complex jobs which are, in turn, represented as workflows [1]. Such workflows can be defined as a Direct Acyclic Graph (DAG) [2]. In DAG, nodes are represented as tasks and the edges indicate the data dependency between two nodes. In the aforementioned fields, the workflows are large and complicated in structure [3]. This necessitates more storage and higher computational power. Therefore, various projects are designed in a grid environment such as GrADS, Pegasus, or ASKALON for pre-processing and managing the complex workflows. In recent years, cloud computing has been noted to be the most suitable for executing large workflow applications due to its provision of unlimited resources using the virtualization concept [4].  
In cloud computing, users are charged on a pay-per-use model [5, 6] because resources are set up dynamically and are based on the need of workflows and their deadlines [7]. This allows for a better management of both the quality and quantity of resources than other distributed systems, such as grids, can achieve [8, 9]. However, in order to schedule large scientific-workflow applications, efficient scheduling schemes for processing are deemed essential.  
In cloud computing [10], the primary challenge of workflow scheduling [11] lies in assigning each job or task to a virtual machine for execution [12]. Figure 1 illustrates the sample workflow, defined by a set of nine tasks with dependencies between tasks. The output of one task serves as the input for another task.  
Scheduling plays a vital role in minimizing energy consumption, makespan, and cost while maximizing resource utilization. The existing methods are inadequate for finding optimal solutions. To address this issue, this paper presents a dynamic priority approach for workflow scheduling in cloud computing.  
The main contributions of this paper are as follows:  
1. Identifying the best workflow is a challenging task in scheduling. In this paper, based on the study, we select \"Cybershake\" as the best workflow;
2. Handling dynamic traffic in cloud computing is very difficult. Static thresholds fail to schedule tasks optimally. In our proposed approach, we define a dynamic threshold value based on the task execution time, workflow model, and availability of resources;
3. In this paper, we present an extensive literature review for selecting multi-objective parameters for task scheduling;
4. We exemplify our proposed work with standard numerical values to prove the efficiency of a framework for cloud computing;
5. To make our proposed algorithm more realistic, we include the data transfer time between tasks;
6. To calculate the efficiency of proposed algorithm, simulation results are compared with the five existing algorithms.  
The article is organized as follows. Section Two discusses the related work in brief. Section Three presents the scheduling problem formation and Section Four elucidates the proposed algorithm. Section Five presents the results of the simulation and performance evaluations and Section Six notes the conclusions derived from the current study.