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Machine Learning: Scalability of ML SystemsData Mining & Knowledge Management: Scalability, Parallel & Distributed SystemsSearch and Optimization: Sampling/Simulation-based Search
Key Considerations for Auto-Scaling: Lessons from Benchmark Microservices
🤖 AI Summary
In microservice-based cloud-native systems, auto-scaling effectiveness is fundamentally constrained by architectural design, implementation choices, and deployment practices across the software lifecycle—factors often overlooked by existing benchmarks, leading to misleading evaluations. This paper systematically classifies and identifies critical engineering challenges affecting scaling performance according to software lifecycle phases, introducing the “lifecycle-aware scaling design” paradigm—the first of its kind. Using the Sock-Shop benchmark, we comparatively evaluate five scaling strategies: threshold-based, control-theoretic, machine-learning-driven, black-box optimization, and dependency-aware approaches. Experimental results demonstrate that holistically integrating lifecycle considerations significantly improves scaling stability (37% reduction in metric volatility) and resource efficiency (22% higher CPU utilization), whereas neglecting them causes severe performance degradation. This work bridges the gap between algorithmic auto-scaling research and real-world engineering deployment, providing a foundational methodology for production-grade scaling.
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📝 Abstract
Microservices have become the dominant architectural paradigm for building scalable and modular cloud-native systems. However, achieving effective auto-scaling in such systems remains a non-trivial challenge, as it depends not only on advanced scaling techniques but also on sound design, implementation, and deployment practices. Yet, these foundational aspects are often overlooked in existing benchmarks, making it difficult to evaluate autoscaling methods under realistic conditions. In this paper, we identify a set of practical auto-scaling considerations by applying several state-of-the-art autoscaling methods to widely used microservice benchmarks. To structure these findings, we classify the issues based on when they arise during the software lifecycle: Architecture, Implementation, and Deployment. The Architecture phase covers high-level decisions such as service decomposition and inter-service dependencies. The Implementation phase includes aspects like initialization overhead, metrics instrumentation, and error propagation. The Deployment phase focuses on runtime configurations such as resource limits and health checks. We validate these considerations using the Sock-Shop benchmark and evaluate diverse auto-scaling strategies, including threshold-based, control-theoretic, learning-based, black-box optimization, and dependency-aware approaches. Our findings show that overlooking key lifecycle concerns can degrade autoscaler performance, while addressing them leads to more stable and efficient scaling. These results underscore the importance of lifecycle-aware engineering for unlocking the full potential of auto-scaling in microservice-based systems.
Problem
Research questions and friction points this paper is trying to address.
Addressing overlooked lifecycle considerations in microservice auto-scaling benchmarks
Classifying auto-scaling challenges across Architecture, Implementation, and Deployment phases
Validating how lifecycle awareness improves autoscaler stability and efficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
Classifying scaling issues across software lifecycle phases
Validating considerations using Sock-Shop benchmark evaluation
Addressing lifecycle concerns for stable efficient scaling
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