TPS: A task placement strategy for big data workflows

Workflow makespan is the total execution time for running a workflow in the Cloud. The workflow makespan significantly depends on how the workflow tasks and datasets are allocated and placed in a distributed computing environment such as Clouds. Incorporating data and task allocation strategies to minimize makespan delivers significant benefits to scientific users in receiving their results in time. The main goal of a task placement algorithm is to minimize the total amount of data movement between virtual machines during the execution of the workflows. In this paper, we do the following: 1) formalize the task placement problem in big data workflows; 2) propose a task placement strategy (TPS) that considers both initial input datasets and intermediate datasets to calculate the dependency between workflow tasks; and 3) perform extensive experiments in the distributed environment to demonstrate that the proposed strategy provides an effective task distribution and placement tool.