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Optimal Workflow Execution in Grid Environments

This paper describes an original approach to optimally execute computational tasks in Grid environments. Tasks are represented as workflows that define interactions between different services. We extend the functional description of services with non-functional properties, allowing to specify the resource requirements of services for given inputs. Based on such annotations, we derive a mathematical model to estimate the execution costs of a workflow. Moreover, we present a genetic algorithm that optimally distributes the execution of a workflow in a Grid, supporting the installation of software components on demand, in order to fulfill user requirements, such as a limit on the total workflow execution time. We implemented a testbed to validate our approach on randomly generated workflows in simulated Grids. 2