  Cloud computing provides engineers or scientists a place to run complex
computing tasks. Finding a workflow's deployment configuration in a cloud
environment is not easy. Traditional workflow scheduling algorithms were based
on some heuristics, e.g. reliability greedy, cost greedy, cost-time balancing,
etc., or more recently, the meta-heuristic methods, such as genetic algorithms.
These methods are very slow and not suitable for rescheduling in the dynamic
cloud environment. This paper introduces RIOT (Randomized Instance Order
Types), a stochastic based method for workflow scheduling. RIOT groups the
tasks in the workflow into virtual machines via a probability model and then
uses an effective surrogate-based method to assess a large amount of potential
scheduling. Experiments in dozens of study cases showed that RIOT executes tens
of times faster than traditional methods while generating comparable results to
other methods.
