# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
###### Abstract  
A heterogeneous system can be portrayed as a variety of unlike resources that can be locally or geologically spread, which is exploited to implement data-intensive and computationally intensive applications. The competence of implementing the scientific workflow applications on heterogeneous systems is determined by the approaches utilized to allocate the tasks to the proper resources. Cost and time necessity are evolving as different vital concerns of cloud computing environments such as data centers. In the area of scientific workflows, the difficulties of increased cost and time are highly challenging, as they elicit rigorous computational tasks over the communication network. For example, it was discovered that the time to execute a task in an unsuited resource consumes more cost and time in the cloud data centers. In this paper, a new cost- and time-efficient planning algorithm for scientific workflow scheduling has been proposed for heterogeneous systems in the cloud based upon the Predict Optimistic Time and Cost (POTC). The proposed algorithm computes the rank based not only on the completion time of the current task but also on the successor node in the critical path. Under a tight deadline, the running time of the workflow and the transfer cost are reduced by using this technique. The proposed approach is evaluated using true cases of data-exhaustive workflows compared with other algorithms from written works. The test result shows that our proposed method can remarkably decrease the cost and time of the experimented workflows while ensuring a better mapping of the task to the resource. In terms of makespan, speedup, and efficiency, the proposed algorithm surpasses the current existing algorithms--such as Endpoint communication contention-aware List Scheduling Heuristic (ELSH), Predict Earliest Finish Time (PEFT), Budget-and Deadline-constrained heuristic-based upon HEFT (BDHEFT), Minimal Optimistic Processing Time (MOPT) and Predict Earlier Finish Time (PEFT)--while holding the same time complexity. h 24 August 2021 10 October 2021  
as per the user's requirements. The cloud providers Amazon EC2, Digital Ocean, Microsoft Azure, Rackspace Open Cloud, Google Compute Engine, IBM Smart Cloud Enterprise, Cloud Stack, Open Nebula, and Go Grid offer resources/services [3]. Scientific workflows are depicted by a directed acyclic graph (DAG) in which nodes signify tasks in the application and the edges signify dependencies between the tasks. Each node label represents the time of the execution of the task, and the edges represent the transfer costs among the tasks. The scientific workflows are specifically associated with scientific denominations like bioinformatics and biodiversity. BioBIKE and astronomy present a robust circumstance for the utilization of cloud implementation. Workflow planning is a series consisting of allocating the dependent tasks on the appropriate resources in such a way that the complete workflow will be able to finish its implementation inside the user's mentioned quality of service (QoS) constraints. The processing of workflows involves enormous computation and communication costs. The aim is to allocate the objectives onto appropriate virtual machines and sort the sequence of implementations so that the task priority needs are satisfied and a minimum implementation time and reduced cost are obtained. Moreover, realistic scheduling approaches must use only a few resources and must have less time complexity. The task-scheduling problem is known to be NP-complete, and the challenges in the field are mainly focused on getting low-complexity heuristics that yield excellent schedules. Scheduling algorithms are usually grouped into two types, namely heuristic and meta-heuristic. For a broad range of given problems, heuristic is defined as an approximate solution and problem-dependent and metaheuristics is defined as problem-independent where a solution can be received for which it can be proved its closeness to the optimal solution.