# Cost and Time Economical Planning Algorithm for Scientific Workflows in Cloud Computing
###### Abstract
The heuristic algorithms that are frequently operated in a heterogeneous environment have varied and versatile virtual machines, such as cloud data centers, etc. They consist of duplication-based, list-based, and clustering-based algorithms. Some of the meta-heuristic algorithm examples are as follows--genetic algorithm (GA), firefly optimization (FO), artificial neural network (ANN), cat swarm optimization (CSO), particle swarm optimization (PSO), simulated annealing (SA), tabu search (TS), and ant colony optimization (ACO) can provide an optimal solution. On relating the heuristic technique with the metaheuristic technique, when the count of tasks is multiplied, the makespan of the metaheuristic techniques proliferates continuously. The heuristic algorithm can be separated into a duplication-based scheduling algorithm [4], a list-scheduling algorithm, and a clustering scheduling algorithm. There are two important stages in the list-scheduling algorithm. In the first stage, the priority is set to the tasks by computing the rank to every task in the DAG. In the next stage, the corresponding virtual machine is chosen to complete the task. The methodology behind the clustering-based scheduling technique is to find the tasks in the DAG which would be executed on the self-same virtual machine (VM). There are two phases in the clustering-based technique. Phase 1 is the clustering phase, where the tasks are segregated into a collection of disjoint clusters. Phase 2 is the merging phase, where the clusters are merged so that they can be assigned or allocated to the confined measure of processors at runtime. One of the best widespread heuristic clustering-based algorithms is the dominant sequence clustering (DSC) [5]. The technique that replicates the parent tasks by using the time in which the processors are idle is termed a duplication-based scheduling algorithm [6]. This collection of algorithms performs task scheduling by the allocation of its redundant tasks. Hence, this technique can decrease the inter-related communication overhead. Conversely, these techniques are impractical due to their increased time complexity. The genetic algorithm (GA) which is a meta-heuristic algorithm, is renowned and extensively discussed. In this type, there are three stages, which are called the selection, the crossover, and the mutation stage. In the first one (selection stage), initially, the population set is generated and the parents are selected from 'individuals' of the group. In the crossover stage, the parents hybridize to yield children that are 'offspring', and in the mutation stage, the children could be changed as per the mutation rules. The scheduling algorithms aim to reduce the total execution time without making a note of the resource access cost. However, resources of different competencies at different costs are provided by the service provider in the cloud environment. More rapid resources are always more expensive than the slow one. Therefore, various planning techniques of the same workflow using dissimilar hardware/resources may result in a dissimilar schedule length ratio and a dissimilar price. Therefore, the workflow planning issue in cloud computing entails both cost and time limitations which need to be fulfilled as per the user's requirement. The budget constraint confirms that the cost mentioned by the user has not been exceeded, and the deadline constraint confirms that the workflow is implemented within the specified time limit. Decent heuristic attempts have been made to handle both these constraints equally and achieve a better result. The two objectives in workflows are minimizing the makespan and minimizing the implementation costs. In this paper, a new Predict Optimistic Time and Cost (POTC) is recommended to plan workflow applications to cloud computing services that reduce the implementation cost (i.e., the sum of transfer cost and VM price) and the total execution time (i.e., the addition of data transmission time and execution time) for executing the scientific workflows simultaneously.