Optimizing Cloud Workloads: Autoscaling with Reinforcement Learning
Pratik Mishra, Sandeep Hans, Diptikalyan Saha, Pratibha Moogi
Abstract
By 2027, over 50 % of enterprises are expected to adopt industry cloud platforms [1], driving potential EBITDA value of $3 trillion by 2030 [2]. In this landscape, software providers rely on Infrastructure-as-a-Service (IaaS) providers to access tailored virtualized resources based on usage. Optimizing resource utilization is crucial to reducing operating costs and maintaining quality standards for SaaS and IaaS providers. This creates an essential need for dynamic scaling mechanisms to adjust resources according to workload variations. The Kubernetes resource Horizontal Pod Autoscaler (HPA) has limitations in scaling applications. However, AI-based algorithms, particularly Reinforcement Learning (RL), offer promising solutions. AI-based methods excel in overcoming fixed parameter constraints, handling sudden load spikes, and supporting custom parameters. We present an RL-based framework for auto scaling applications, demonstrating results from experimental evaluation.