Cloud attempt to reduce the execution time without

Cloud
computing is a platform for various kind of applications with different Quality
of  Service (QoS). Generally, cloud
services can be classified as Software as a service (SaaS),Platform as a
Service (PaaS),  Infrastructure as a
service(IaaS) .Saas is browser based interface that allows users to alter the
documents online(e.g Web based email, Dropbox, or Netflix) .Paas provides
different components to develop applications in cloud(e.g Google app engine).
IaaS provides the resources such as virtualization components, networks and
servers ( e.g Windows Azure, Amazon web services). These services makes
execution of scientific applications easier in cloud computing . Usally, Scientific
Workflow Management System (SWfMS) software is used to handle scientific
applications and also reduce complexity to execute data intensive applications
.These workflow managements systems are obtained from some Grid
projects(Pegasus, ASKALON and GrADS). 

Workflow scheduling is very important task
for representing complex applications in cloud computing. Generally, Workflows  are  a sequence
of  tasks related by data and control
-flow dependencies and it can be 
represented by Directed Acyclic Graph (DAG) . These  workflows are applied in different scientific
areas such as biology and astronomy. However, size of scientific workflows is
increasing that makes difficulty for managing big data , more effective and
scalable infrastructures are required to execute complex workflows within a  time. Therefore, scheduling techniques are
necessary for executing workflows .

 

Workflow
scheduling is the procedure of mapping tasks on  appropriate resources to achieve performance
criteria like QoS constraints. Initially, in grid environment, some  scheduling techniques  attempt to reduce  the execution time without measuring cost of
resources. But , in cloud environment ,different capabilities are provided by
service 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 be
very difficult to obtain an optimal schedule because huge  communication and computation cost is
required for scheduling of workflows. Some factors  are considered for scheduling problem
solutions ( i.e load balancing, resource utilization and services in scheduling
decision ).

In
distributed systems, Workflows  establish
a common  model for characterize a wide area  of scientific applications. The main issue of  Distributed and parallel systems environment
is efficient utilization of  resources.
For this reason, main parameters considered in cloud computing  is execution time and cost. Commonly, faster
resources are costlier than slower one. Therefore, Scheduling algorithms and
provisioning of resources are necessary for minimizing the makespan ,cost and
also improves utilization of 
resources.  Different techniques
like Ant colony optimization, Shuffled frog leaping algorithm, Particle Swarm optimization   are used to solve the problem of workflow
scheduling .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 in
numerous areas like Aircrafts wing, Feature selection ,Engineering ,Unit
commitment problem ,  and  Constrained optimization problems.Further,
we applied a Sine Cosine algorithm in workflow scheduling for optimal
scheduling. The main aim of Sine  Cosine
algorithm is to reduce the execution time of workflow scheduling . Moreover,
Performance of algorithm is analyzed