Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Scalability of ML SystemsMultiagent Systems: Adversarial Agents
STaleX: A Spatiotemporal-Aware Adaptive Auto-scaling Framework for Microservices
🤖 AI Summary
Traditional auto-scaling methods suffer from inaccurate resource allocation and frequent SLO violations due to the dynamic spatiotemporal characteristics of microservices. To address this, we propose a service-level adaptive autoscaling framework driven by SLOs. Our approach introduces a novel weighted PID controller architecture that jointly models spatial dependencies among services and temporal evolution of workloads—marking the first integration of both dimensions into scaling decisions. A supervision unit dynamically adjusts per-service controller weights, enabling fine-grained, context-aware adaptation. The framework unifies control-theoretic principles, spatiotemporal machine learning modeling, and heuristic optimization, and is natively compatible with Kubernetes. Experimental evaluation on a real-world cluster demonstrates that our method reduces resource consumption by 26.9% compared to Kubernetes’ Horizontal Pod Autoscaler (HPA), significantly mitigates SLO violations, and improves both performance and cost efficiency.
Technology Category
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Microservices Architecture
Auto-scaling
Resource Allocation
Innovation
Methods, ideas, or system contributions that make the work stand out.
STaleX
Dynamic Resource Allocation
Microservices Optimization
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