The WMS aims to achieve the optimum schedule as per SLA [12; 13; 14]. There are three main advantages of allocating workflow applications to the Cloud [8]: (i) the Cloud facilitates the automation of workflow processing by utilization of cloud services; (ii) cost-effectiveness of hardware expenditure due to shared resources; and (iii) high degree of user satisfaction with reduced processing cost and time [15]. Some issues in workflow allocation are listed, such as precedence constraints, communication cost, depth level, idle slots, and so on [16]. WFA should optimize the makespan, cost, flowtime, response time, energy usage, and resource utilization by respecting task dependencies, data transfer needs, and other requirements. The cloud resources' dynamic nature and large number of mappings make this problem NP-hard [17]. Therefore, many heuristic and metaheuristic algorithms have been developed for WFA problems, which can obtain near-optimal solutions in real-time. This work proposed a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize the makespan in clouds. Makespan for a workflow execution in cloud computing signifies the aggregate time required to finish all tasks inside the workflow, ranging from the initiation of the first task to the end of the last task. It is a crucial performance metric for evaluating the efficacy of workflow tasks and resource allocation strategies in a cloud environment. The workflows considered in the work are independent of each other and may be submitted by different cloud users. The model considers the inter-task communication between the cooperative workflow tasks. The task prioritization and allocation phases are the proposed model's two phases. Workflows are divided into the appropriate number of partitions based on level attributes to start the task prioritization phase. The major motivations and contributions have been listed as follows:  
* This work proposes a Levelized Multiple Workflow Heterogeneous Earliest Finish Time (LMHEFT) model to optimize the makespan in the cloud computing environment. LMHEFT exploits parallelism at the workflow and task levels within the workflow, if available. * In the task prioritization phase, after slicing the multiple workflow tasks, precedence constraints are handled using the level attribute and upward rank values of the workflow tasks. This will ensure the execution of all parents before their children with the better exploitation of parallelism to enhance performance. Additionally, this way, it is quite suited for requests from interactive cloud users. * In the allocation phase, the best-suited virtual machine is determined, which offers the lowest finish time for each task to minimize the workflow task's completion time by having inter-task communication between the cooperative workflow tasks. * Employing a ranking-based mapping scheme on workflow tasks surely reduces the processing time, thereby approaching the optimum value of the makespan. * The comparative performance evaluation of LMHEFT has been conducted with the competitive models from the literature implemented in MATLAB, i.e., DLS [18] and HEFT [9] (implementing their multiple workflow versions as suggested by [19]). * Statistical analysis has been carried out to test the significance level of the improvement in the proposed LMHEFT model among the other models considered in the simulation study by using normality tests (Kolmogorov-Smirnov and Shapiro-Wilk tests), the Levene test for homogeneity verification, and the Kruskal-Wallis test for comparing models to verify the significant improvement. The remainder of this paper is organized as follows: Section 2 discusses related work. Section 3 includes the multiple workflow model, cloud workflow framework, cloud resource model, parameter estimation, and problem definition. In Section 4, we have proposed an LMHEFT model and presented an algorithmic template with an illustration, along with its time complexity. The experimental results and findings are discussed in Section 5.