DISTRIBUTED RADIOTHERAPY SIMULATION WITH THE WEBCOM WORKFLOW SYSTEM
Accurate radiotherapy plans are a vital tool in combating cancer. The verification of such plans is a computationally intensive task, and providing clinical experts with access to sufficient resources to conduct plan verification simulations in a suitable and timely manner is a genuine challenge. In this paper we present a new approach to the problem, incorporating the Monte Carlo method for treatment verification. A fully integrated radiotherapy treatment verification workflow built on the BEAM simulation package has been developed within the scope of this work. The Monte Carlo approach is recognized as being superior to the standard clinical techniques available. To be useful in clinical practice, accurate results must be generated within a short time frame. Consequently, turnaround times must be predictable, and results must be of a consistently high standard. These requirements are the key challenges that drive this work. The development of this application is being conducted within the context of the Webcom project. Webcom is an interpreter for a graph-oriented model of computing, implemented as a distributed virtual machine. This platform has been used to construct a workflow tool suite and a novel methodology for dynamic resource federation. These components are applied to the execution of Monte Carlo radiotherapy simulation application on heterogeneous dynamically coordinated resources. The Webcom-based model of workflow management facilitates the execution of resource intensive workflows and provides a basis for the development of scalable services in the heterogeneous environments formed through the dynamic aggregation of mixed autonomous resources. We discuss the motivation behind the project and present the methodology, describe the software design of the current implementation, and demonstrates the utility of the system via experiments conducted in a real and deeply heterogeneous testbed environment.
distributed radiotherapy webcom distributed monte carlo optimisation beamnrc cycle-scavenging heterogeneous computing