STaleX: A Spatiotemporal-Aware Adaptive Auto-scaling Framework for Microservices

TL;DR

STaleX is an adaptive auto-scaling framework for microservices that uses spatiotemporal features and control theory to optimize resource allocation and reduce SLO violations in real-time.

Contribution

It introduces a novel spatiotemporal-aware auto-scaling framework combining control theory, machine learning, and heuristics for microservices.

Findings

Reduces resource usage by 26.9% compared to Kubernetes HPA

Effectively minimizes SLO violations in microservice environments

Adapts to workload variations through dynamic weight adjustments

Abstract

While cloud environments and auto-scaling solutions have been widely applied to traditional monolithic applications, they face significant limitations when it comes to microservices-based architectures. Microservices introduce additional challenges due to their dynamic and spatiotemporal characteristics, which require more efficient and specialized auto-scaling strategies. Centralized auto-scaling for the entire microservice application is insufficient, as each service within a chain has distinct specifications and performance requirements. Therefore, each service requires its own dedicated auto-scaler to address its unique scaling needs effectively, while also considering the dependencies with other services in the chain and the overall application. This paper presents a combination of control theory, machine learning, and heuristics to address these challenges. We propose an adaptive auto-scaling framework, STaleX, for microservices that integrates spatiotemporal features, enabling real-time resource adjustments to minimize SLO violations. STaleX employs a set of weighted Proportional-Integral-Derivative (PID) controllers for each service, where weights are dynamically adjusted based on a supervisory unit that integrates spatiotemporal features. This supervisory unit continuously monitors and adjusts both the weights and the resources allocated to each service. Our framework accounts for spatial features, including service specifications and dependencies among services, as well as temporal variations in workload, ensuring that resource allocation is continuously optimized. Through experiments on a microservice-based demo application deployed on a Kubernetes cluster, we demonstrate the effectiveness of our framework in improving performance and reducing costs compared to traditional scaling methods like Kubernetes Horizontal Pod Autoscaler (HPA) with a 26.9% reduction in resource usage.