WORKFLOW SCHEDULING ACCORDING TO DATA DEPENDENCIES IN COMPUTATIONAL CLOUDS
Hamid Saadatfar,Batoul Khazaie
Bottleneck task,Computational volume,Data-intensive applications,Resource allocation,Sensitive task,Workflow scheduling
The number of applications needing big data is on the rise nowadays, where big data processing tasks are sent
as workflows to cloud computing systems. Considering the recent advances in the Internet technology, cloud
computing has become the most popular computing technology. The scheduling approach in cloud computing
environments has always been a topic of interest to many researchers. This paper proposes a new scheduling
algorithm for data-intensive workflows based on data dependencies in computational clouds. The proposed
algorithm tries to minimize the makespan by considering the details of the workflow structure and virtual
machines. The concepts and details defined and considered in this study have received less emphasis in previous
works. According to the results, the proposed algorithm reduced the duration of communication between tasks
and runtimes by taking into account the features of data-intensive workflows and proper task assignment.
Consequently, it reduced the total makespan in comparison with previous algorithms.