Cloud computing is a new prototype for enterprises which can effectively assist the execution
of tasks. Task scheduling is a major constraint which greatly influences the performance of
cloud computing environments. The cloud service providers and consumers have different
objectives and requirements. For the moment the load and availability of the resources vary
dynamically with time. Therefore in the cloud environment scheduling resources is a
complicated problem. Moreover task scheduling algorithm is a method by which tasks are
allocated or matched to data center resources. All task scheduling problems in a cloud
computing environment come under the class of combinatorial optimization problems which decide
searching for an optimal solution in a finite set of potential solutions. For a combinatorial
optimization problem in bounded time exact algorithms always guarantee to find an optimal
solution for every finite size instance. These kinds of problems are NP-Hard in nature.
Moreover for the large scale applications an exact algorithm needs unexpected computation
time which leads to an increase in computational burden. However the absolutely perfect
scheduling algorithm does not exist because of conflicting scheduling objectives. Therefore
to overcome this constraint heuristic algorithms are proposed. In workflow scheduling problems
search space grows exponentially with the problem size. Heuristics optimization as a search
method is useful in local search to find good solutions quickly in a restricted area. However
the heuristics optimization methods do not provide a suitable solution for the scheduling
problem. Researchers have shown good performance of metaheuristic algorithms in a wide range of
complex problems. In order to minimize the defined objective of task resource mapping improved
versions of Particle Swarm Optimization (PSO) are put in place to enhance scheduling
performance with less computational burden. In recent years PSO has been successfully applied
to solve different kinds of problems. It is famous for its easy realization and fast
convergence while suffering from the possibility of early convergence to local optimums. In
the proposed Improved Particle Swarm Optimization (IPSO) algorithm whenever early convergence
occurs the original particle swarm would be considered the worst positions an individual
particle and worst positions global particle the whole swarm have experienced.
