Cloud attempt to reduce the execution time without

Cloudcomputing is a platform for various kind of applications with different Qualityof  Service (QoS). Generally, cloudservices can be classified as Software as a service (SaaS),Platform as aService (PaaS),  Infrastructure as aservice(IaaS) .Saas is browser based interface that allows users to alter thedocuments online(e.g Web based email, Dropbox, or Netflix) .Paas providesdifferent components to develop applications in cloud(e.g Google app engine).IaaS provides the resources such as virtualization components, networks andservers ( e.

g Windows Azure, Amazon web services). These services makesexecution of scientific applications easier in cloud computing . Usally, ScientificWorkflow Management System (SWfMS) software is used to handle scientificapplications and also reduce complexity to execute data intensive applications.

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These workflow managements systems are obtained from some Gridprojects(Pegasus, ASKALON and GrADS).  Workflow scheduling is very important taskfor representing complex applications in cloud computing. Generally, Workflows  are  a sequenceof  tasks related by data and control-flow dependencies and it can be represented by Directed Acyclic Graph (DAG) . These  workflows are applied in different scientificareas such as biology and astronomy. However, size of scientific workflows isincreasing that makes difficulty for managing big data , more effective andscalable infrastructures are required to execute complex workflows within a  time. Therefore, scheduling techniques arenecessary for executing workflows .

 Workflowscheduling is the procedure of mapping tasks on  appropriate resources to achieve performancecriteria like QoS constraints. Initially, in grid environment, some  scheduling techniques  attempt to reduce  the execution time without measuring cost ofresources. But , in cloud environment ,different capabilities are provided byservice provider at varied cost. Thus, same workflows using distinct  resources  results in different cost and  execution time . Since, scheduling problem is  NP-hard problem and it can bevery difficult to obtain an optimal schedule because huge  communication and computation cost isrequired for scheduling of workflows. Some factors  are considered for scheduling problemsolutions ( i.e load balancing, resource utilization and services in schedulingdecision ).Indistributed systems, Workflows  establisha common  model for characterize a wide area  of scientific applications.

The main issue of  Distributed and parallel systems environmentis efficient utilization of  resources.For this reason, main parameters considered in cloud computing  is execution time and cost. Commonly, fasterresources are costlier than slower one.

Therefore, Scheduling algorithms andprovisioning of resources are necessary for minimizing the makespan ,cost andalso improves utilization of resources.  Different techniqueslike Ant colony optimization, Shuffled frog leaping algorithm, Particle Swarm optimization   are used to solve the problem of workflowscheduling .These techniques try to reduce the makespan (execution time)  and cost of workflows ,but still more works need for  efficient scheduling.

Sine cosine optimization algorithm is applied innumerous areas like Aircrafts wing, Feature selection ,Engineering ,Unitcommitment problem ,  and  Constrained optimization problems.Further,we applied a Sine Cosine algorithm in workflow scheduling for optimalscheduling. The main aim of Sine  Cosinealgorithm is to reduce the execution time of workflow scheduling . Moreover,Performance of algorithm is analyzed