Cost optimized provisioning of elastic resources for application workflows

Workflow technologies have become a major vehicle for easy and efficient development of scientific applications. In the meantime, state-of-the-art resource provisioning technologies such as cloud computing enable users to acquire computing resources dynamically and elastically. A critical challenge in integrating workflow technologies with resource provisioning technologies is to determine the right amount of resources required for the execution of workflows in order to minimize the financial cost from the perspective of users and to maximize the resource utilization from the perspective of resource providers. This paper suggests an architecture for the automatic execution of large-scale workflow-based applications on dynamically and elastically provisioned computing resources. Especially, we focus on its core algorithm named PBTS (Partitioned Balanced Time Scheduling), which estimates the minimum number of computing hosts required to execute a workflow within a user-specified finish time. The PBTS algorithm is designed to fit both elastic resource provisioning models such as Amazon EC2 and malleable parallel application models such as MapReduce. The experimental results with a number of synthetic workflows and several real science workflows demonstrate that PBTS estimates the resource capacity close to the theoretical low bound.

► PBTS enables workflow execution on elastic resource provisioning environments.
► PBTS estimates the near-optimal resource requirements to minimize the financial cost.
► PBTS has a polynomial time complexity and scales well to very large workflows.
► PBTS adjusts the resource capacity against the runtime status of applications and resources to reduce the financial cost.