## 2 Related Work
### Multiple Workflow Allocation Model  
Multiple workflow processing is producing an effective schedule. The approach provided in [66] introduces a multiple workflow allocation model based on aggregated DAGs that assign tasks based on depth level. The workflow tasks, in this case, are not communicative, and there is no inter-task communication at all. In [20], the authors present four methods, i.e., sequential (one workflow after another in series), search-gap (order is sequential, but idle slots between the tasks of previously assigned workflows are utilized by upcoming workflow tasks without interfering with their starting time), interleave (search gap approach is used, but the scheduler gives a turn to all workflows to assign their task in the vacant spaces on machines), and merge-based (merges the workflows into a single one and then uses the interleaving approach for assignment). Task prioritizing and machine selection use a modified Path Clustering Heuristic (PCH) for comparative study. Other works [67] presented the Multiple Workflow Grid Scheduling 4 stages (MWGS4) and MWGS2 workflow allocation strategies. Several stages are labeling, adaptive allocation, prioritizing, and parallel VM allocation. In [68], the authors provide two policies focused on fairness optimization and four rules for DAG allocation to address the issues in the problem. For trustworthy scientific workflow allocation in the Cloud, a RANK hierarchical consideration, communication contention with task dependencies (CCRH), has been devised [69] with the target of maximizing reliability and enhancing fault tolerance. A novel approach was introduced in [70] by dynamic task rearrangement and rescheduling for allocating various workflows while taking resource efficiency and resilience into account. Rearranging things increases the robustness. To investigate the impact of time constraints on the effectiveness of task allocation, the authors of [71] presented a cluster-based strategy for workflows having soft deadlines. In order to meet the user's urgent expectations, the authors of this study discuss how to accommodate idle time slots. In order to save energy and time, a stochastic technique for multiple DAGs was developed [72]. The literature has also proposed a static level-based batch scheduling (SLBBS) method [14] that reduces the turnaround time with inter-task communication. Additionally, its effectiveness has been assessed with other techniques [11]. SLBBS is enhanced by utilizing the idle slots among the previous level tasks with the tasks of subsequent levels [73]. In [74], the authors have a multi-workflow method to optimize the execution time of the tasks and resource utilization using a genetic algorithm, while in [75], the authors proposed a novel multiple-workflow allocation scheme using multi-criteria decision-making strategy to optimize the cost and utilization in the cloud environment. In [76], the authors present a real-time multiple workflow scheduling (RMWS) strategy in a cloud computing environment to minimize the rental cost of the resources. In [77], the authors developed a novel technique, the security prioritized multiple workflow allocation (SPMWA) model, for cloud computing environments with the aim of minimizing the failure probability. A multiple workflow model is developed using the multi-objective evolutionary algorithm to optimize time, cost, and energy in cloud environments [78]. Another multi-workflow allocation model is reported in the domain for optimizing three objectives, namely, tardiness, energy, and cost, for hybrid clouds [79].