RIOT: a Stochastic-based Method for Workflow Scheduling in the Cloud

TL;DR

This paper introduces RIOT, a stochastic workflow scheduling method for cloud computing that significantly speeds up scheduling while maintaining comparable quality to existing approaches.

Contribution

The paper presents RIOT, a novel stochastic scheduling algorithm that improves speed and efficiency over traditional heuristic and meta-heuristic methods in cloud environments.

Findings

RIOT executes tens of times faster than traditional methods.

RIOT produces comparable scheduling results to existing approaches.

Experiments demonstrate RIOT's effectiveness across multiple case studies.

Abstract

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.