On Efficient Resource Use for Scientific Workflows in Clouds
Abstract
The abundance of cloud resources has enabled not only web applications but also scientific applications to easily scale to meet their goals, such as performance and cost. However, the decision on such scaling (resource management) is very complicated, often resulting in inefficient use of resources, due to the complex and large-scale nature of scientific workflows. In this On Efficient Resource Use for Scientific Workflows in Clouds paper, we present RDAS+ as a resource-sensitive scheduling algorithm to optimize resource efficiency to perform scientific workflows in clouds.
On Efficient Resource Use for Scientific Workflows in Clouds RDAS+ maximizes resource utilization by allocating the minimum number of resources (virtual machines or cloud VMs) with little time-consuming sacrifice (makespan). This optimization eventually leads to cost efficiency on pay-per-use cloud resources. RDAS+ consists of partitioning steps, resource allocation and task scheduling to realize such optimization. In comparison with three existing algorithms, we evaluated RDAS+ using five types of real-world scientific workflows.
System Configuration
Conclusion
In this paper, we summarized research issues for scientific workflow systems in cloud data management. We drew three directions for research that are data storage, data placement and data replication. We analyzed the existing research problems briefly in each direction, introduced promising methodologies and summarized state-of – the-art approaches.
