# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
## 2 Literature Survey  
In this section, a review of task scheduling algorithms in scientific workflows has been studied mostly on list-based heuristics. For the past few years, research has focused on the workflow scheduling algorithm, in order to discover suboptimal results in a reasonably short duration. High-quality schedules are normally generated at a sensible cost using list-based scheduling heuristics. List-based scheduling heuristics have a lower complexity of time, and their results signify a lesser processor load when compared to other scheduling strategies, like clustering algorithms and task duplication strategies [8; 9; 10]. Many heuristic algorithms like Heterogeneous Earlier Finish Time (HEFT), Dynamic Scheduling-of-Bag-of-Tasks-based workflows (DSB), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Critical Path on Processor (CPOP), and Predict Earliest Finish Time (PEFT). Out of these, PEFT outperforms in terms of implementation time. However, all these heuristics try to diminish the implementation time without bearing in mind the cost of executing the workflow tasks. [PERSON] [11] has proposed a list-based scheduling algorithm with the aim of increasing the performance and fast scheduling time. The HEFT algorithm calculates the upward rank for all the tasks in the level and sort in descending order of rank. The task with the highest upward rank is selected first and assigns the same to the processor which reduces its earliest finish time and minimizes the makespan under no restraints. However, their solution does not meet the deadline constraint, and the communication cost is not considered. The HEFT algorithm is tested for heterogeneous systems. [PERSON] [12] presented a Budget-and Deadline-constrained Heterogeneous Earliest Finish Time (BDHEFT) with the aim of reducing the time and cost below the deadline and budget constraint. BDHEFT is an extension of HEFT with heterogeneous resources over the cloud. The algorithm outperforms in terms of monetary cost and makespan. However, they have not taken the transfer cost and communication time into consideration. [PERSON] [13] has proposed an algorithm for heterogeneous computing systems named Predict Earlier Finish Time (PEFT), which uses the optimistic cost table. This study offers a look-ahead on features that offer makespan enhancements without an increase in the time complexity. However, the processor availability, deadline, and budget constraints are not taken into consideration. The time complexity is given as \(O\big{(}n^{2}\big{)}\), and it minimizes the overall makespan. The minimum processing time of the critical path is calculated, and every single task is provided with the best processor. [PERSON] [14] has explicated the Predict Priority Task Scheduling (PPTS) algorithm, which is a list-scheduling heuristic developed for heterogeneous systems, which minimizes the makespan and cost. PPTS also introduces a look-ahead on features similar to the PEFT technique with the same time complexity. This technique has introduced a predict cost matrix while minimizing the makespan. However, deadline and budget constraints are not considered. The study forecasts the nature of the tasks and processors using a look-ahead feature. The algorithm offers better results for a small number of tasks. [PERSON] [15] proposed an algorithm called look-ahead which incorporated the HEFT technique. The processor selection policy is a crucial feature of this algorithm. The algorithm computes the earlier finish time on all the processors for all the child tasks and selects the processor based on the earlier finish time. The look-ahead's structure is similar to that of the HEFT, but the difference is that, for every successor of the present task, EFT is calculated. The time complexity is given as O (v4.p5) for the look-ahead algorithm. The authors concluded that the added stages of forecasting do not result in the improvement of the makespan.