Analysis of Reinforcement Learning for determining task replication in workflows
This addresses efficiency and reliability issues for volunteer computing systems, though it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of unpredictable task execution in volunteer computing workflows by using Reinforcement Learning to dynamically determine the optimal number of task replicas, resulting in a 34% energy savings with only a 4% decrease in workflows meeting a performance bound.
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.