# Structure-Aware Scheduling Methods for Scientific Workflows in Cloud
## 2 Related Work
The authors suggested two algorithms: Deadline Constrained Critical Path (DCCP) and Proportional Deadline Constrained (PDC). The two algorithms resolve workflow scheduling on a dynamically provisioned cloud resource to identify the suitable VMs according to the proportional deadline constraints of the tasks to achieve the minimum cost with a good response time. The problem studied in this work was similar to the bi-objective scheduling issue discussed in Refs. [5; 7], in which a data-locality-aware scheduling algorithm was proposed. The proposed solution considered workload privacy, private cloud power use, and billing cycles of different public clouds. Costs were compared to identify the appropriate cloud environment to decrease the cloud bursting cost while pondering data privacy. Nonetheless, the private cloud component could not utilize the VMs to effectively implement tasks within their anticipated deadlines when the workload privacy percentage increased. Several optimization techniques were proposed to address cloud scientific workflow scheduling. For example, a genetic-based scheduling algorithm was proposed [28] in which the authors improved the performance of the overall performance by reducing repetitions. [PERSON] et al. [29] also proposed a genetic-based approach to decrease the computational cost while accommodating deadline and budget constraints. A hybrid evolutionary algorithm (HEA) [4] was proposed for data assignment and task scheduling issues, which jointly processed such issues to enhance scientific workflow scheduling. [PERSON] et al. [30] suggested a multi-objective hybrid particle swarm optimization algorithm to re solve multi-objective workflow scheduling issues. The algorithm optimizes makespan and cost based on a non-dominance sorting procedure and presents the user with a set of Pareto optimal solutions from which the best solution can be selected. Efficient swarm-based Pareto techniques were investigated in Refs. [1; 2]. [PERSON] et al. [3] suggested a multi-objective scheduling algorithm with fuzzy resource utilization, which optimizes workflow scheduling by studying the data transmission order and the location of task execution (VM). The proposed method decreases execution cost and makespan while adhering to reliability constraints via particle swarm optimization. [PERSON] et al. [31] proposed an evolutionary multi-objective optimization-based algorithm for cloud workflow scheduling. The proposed method aims to reduce the workflow makespan and execution cost by producing schedules with different tradeoffs between makespan and execution cost to enable users to determine the schedule that satisfies their requirements. In Ref. [32], the authors proposed a structure-aware scheduling approach that starts by determining the optimal number of required VMs to schedule the workflow tasks. Typically, due to cost limitations and VMs availability, hiring the optimal number of VMs is not expected to be guaranteed. Based on the available number of VMs (lower-bound), the proposed approach divides the workflow tasks into a set of groups using a level-based strategy. The number of tasks assigned to each group is bounded by the number of available VMs. Accordingly, a group might include tasks from several levels. Such a strategy is used to improve the efficiency of the obtained schedule. However, the efficiency of the proposed method is impacted by the number of used VMs and the complexity of the data dependency constraints. Moreover, there are inherited relationships between the problem presented in this work and the problem of monitoring the service level in the business domain [33]. In such a domain, services are generally represented as a workflow, and an efficient schedule must be obtained to enforce the services level agreements. Accordingly, exploring the relationship between the two problems may lead to tuning the performance of the employed scheduling solution in a cloud computing context.