STaleX: A Spatiotemporal-Aware Adaptive Auto-scaling Framework for Microservices
Majid Dashtbani, Ladan Tahvildari

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…
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Taxonomy
TopicsSoftware System Performance and Reliability · Cloud Computing and Resource Management · Data Stream Mining Techniques
Methodstravel james · Sparse Evolutionary Training
