RIOT: a Stochastic-based Method for Workflow Scheduling in the Cloud
This addresses the need for faster and more adaptable scheduling for engineers or scientists using cloud computing, though it appears incremental as it builds on existing stochastic and surrogate-based techniques.
The paper tackles the problem of workflow scheduling in dynamic cloud environments by introducing RIOT, a stochastic-based method that groups tasks into virtual machines using a probability model and evaluates potential schedules with a surrogate-based approach. Experiments showed RIOT executes tens of times faster than traditional methods while producing comparable results.
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.