# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
## 2 Literature Survey
[PERSON] [16] presented a PEFT algorithm with an improved technique called Improved Predict Earliest Finish Time Algorithm (IPEFT). IPEFT uses two cost tables to calculate the rank, which is named as a Critical Node Cost Table (CNCT) and Pessimistic Cost Table (PCT). The PCT and CNCT are a matrix and denote the number of tasks as rows and the processors' number as the columns, respectively. First, this matrix calculates the priority of a task \(t_{i}\) by Rank\({}_{\text{PCT}}\). Then, the tasks are arranged in decreasing order of Rank\({}_{\text{PCT}}\). One of the big challenges in cloud computing is the effective scheduling of large and computationally complex workflows with several dependent tasks interconnected to each other. [PERSON] [17] has introduced a Minimal Optimistic Processing Time (MOPT) algorithm which is a list-based heuristic scheduling technique with an optimized duplication approach. The optimistic processing time matrix is used in the prioritizing phase to rank the tasks. The mapping of the task to the resource is based on the minimum completion time. The experimental analysis shows that this method provides quality schedules with minimum makespan. This algorithm is suitable for communication-intensive applications, as the communication cost increases among the tasks. However, in this work, the execution cost with respect to deadline and budget constraints is not considered. [PERSON] [18] has proposed an Endpoint communication contention-aware List Scheduling Heuristic (ELSH) to minimize the makespan and the cost. The author has identified that the endpoint communication contention is the major parameter with a large impact on minimizing the workspan. The author has also discussed the manners of address contention,such as Exclusive and Shared Communication. In order to address the contention issue, the author has implemented an exclusive communication technique. In this algorithm, however, the economic cost and the budget and deadline constraints are not considered. [PERSON]. [19] has proposed a two-way strategy that includes historical scheduling and achieves the load balancing of cloud resources. Virtual machines are created based on the Bayes classifier algorithm, and the tasks are matched with the virtual machines. This method saves time in creating virtual machines, and thus the time and the cost are reduced. However, the proposed algorithm is not compared with the standard planning algorithm, such as HEFT, PEFT, BDHEFT, etc. [PERSON]. [20] has proposed an algorithm that minimizes the energy consumption under response time and reliability constraints. The author has formulated the problem as a Non-linear Mixed Integer Programming problem and solved it using heuristic solutions. To reduce the energy consumption, some of the energy inefficient processors are turned off using a processor-merging algorithm and a slack time reclamation algorithm, and the author has also applied the Dynamic Voltage Frequency Scaling (DVFS) technique onto it to reduce the power consumption. However, the proposed approach has not paid much attention to the computation cost. Several existing algorithms have been studied, and it has been found that these algorithms can lead to inefficient scheduling. One of the main issues faced in the above method is that less important tasks are scheduled before higher priority tasks. Moreover, the deadline and budget constraints are not considered. For this reason, in the proposed algorithm, we calculate the rank such that the tasks with higher priority are arranged before the tasks with less significance. The proposed algorithm is discussed in the following section.