Estimating Resource Needs for Time-Constrained Workflows

Workflow technologies have become a major vehicle for the easy and efficient development of science applications. At the same time new computing environments such as the Cloud are now available. A challenge is to determine the right amount of resources to provision for an application. This paper introduces an algorithm named balanced time scheduling (BTS), which estimates the minimum number of virtual processors required to execute a workflow within a user-specified finish time. The resource estimate of BTS is abstract, so it can be easily integrated with any resource description language or any resource provisioning system. The experimental results with a number of synthetic workflows demonstrate that BTS can estimate the computing capacity close to the optimal. The algorithm is scalable so that its turnaround time is only tens of seconds even with workflows having thousands of tasks and edges.