  Executing workflows on volunteer computing resources where individual tasks
may be forced to relinquish their resource for the resource's primary use leads
to unpredictability and often significantly increases execution time. Task
replication is one approach that can ameliorate this challenge. This comes at
the expense of a potentially significant increase in system load and energy
consumption. We propose the use of Reinforcement Learning (RL) such that a
system may `learn' the `best' number of replicas to run to increase the number
of workflows which complete promptly whilst minimising the additional workload
on the system when replicas are not beneficial. We show, through simulation,
that we can save 34% of the energy consumption using RL compared to a fixed
number of replicas with only a 4% decrease in workflows achieving a pre-defined
overhead bound.
