  The advantages of distributing workloads and utilizing multiple distributed
resources are now well established. The type and degree of heterogeneity of
distributed resources is increasing, and thus determining how to distribute the
workloads becomes increasingly difficult, in particular with respect to the
selection of suitable resources. We formulate and investigate the resource
selection problem in a way that it is agnostic of specific task and resource
properties, and which is generalizable to range of metrics. Specifically, we
developed a model to describe the requirements of tasks and to estimate the
cost of running that task on an arbitrary resource using baseline measurements
from a reference machine. We integrated our cost model with the Condor
matchmaking algorithm to enable resource selection. Experimental validation of
our model shows that it provides execution time estimates with 157-171% error
on XSEDE resources and 18-31% on OSG resources. We use the task execution cost
model to select resources for a bag-of-tasks of up to 1024 GROMACS MD
simulations across the target resources. Experiments show that using the
model's estimates reduces the workload's time-to-completion up to ~85% when
compared to the random distribution of workload across the same resources.
