A Q-learning approach for the autoscaling of scientific workflows in the Cloud

Abstract

Autoscaling strategies aim to exploit the elasticity, resource heterogeneity and varied prices options of a Cloud infrastructure to improve efficiency in the execution of resource-hungry applications such as scientific workflows. Scientific workflows represent a special type of Cloud application with task dependencies, high-performance computational requirements and fluctuating workloads. Hence, the amount and type of resources needed during workflow execution changes dynamically over time. The well-known autoscaling problem comprises (i) scaling decisions, for adjusting the computing capacity of a virtualized infrastructure to meet the current demand of the application and (ii) task scheduling decisions, for assigning tasks to specific acquired Cloud resources for execution. Both are highly complex sub-problems, even more because of the uncertainty inherent to the Cloud. Reinforcement Learning (RL) provides a solid framework for decision-making problems in stochastic environments. Therefore, RL offers a promising perspective for designing Cloud autoscaling strategies based on an online learning process. In this work, we propose a novel formulation for the problem of infrastructure scaling in the Cloud as a Markov Decision Process, and we use the Q-learning algorithm for learning scaling policies, while demonstrating that considering the specific characteristics of workflow applications when taking autoscaling decisions can lead to more efficient workflow executions. Thus, our RL-based scaling strategy exploits the information available about workflow dependency structures. Simulations performed on four well-known workflows demonstrate significant gains (25%–55%) of our proposal in comparison with a similar state-of-the-art proposal.