Scheduling scientific workflow tasks in cloud using swarm intelligence

One of the state-of-the art techniques of Cloud Computing is the type of distributed computing system and derives its features. It has been unanimously used by all the organizations as its yields enormous benefits and its features. The scalability and heterogeneity features make the Cloud most suitable for computing scientific workflow tasks as the workflow comprises thousands of tasks and deals with huge amount of data. Many scheduling algorithms have been proposed using different methods to compute the workflow tasks in cloud with different objectives such as minimal makespan, minimal cost, maximal resource utilization etc. In spite of that this paper proposes an algorithm namely Improved Workflow Scheduling using ACO (IWSACO) with variance in WFSACO (WorkFlow Scheduling using Ant Colony Optimization) using one of the swarm intelligence techniques of ACO to obtain better performance.