SLO-Scaler: Uncertainty-Aware SLO-Driven Autoscaling for Microservices

2026-08-18

AI Summary

本文针对微服务自动扩展中满足服务水平目标(SLO)的挑战，提出SLO-Scaler框架，利用贝叶斯LSTM模型预测请求率和延迟，并结合置信区间及依赖图分析来优化扩展决策。

Abstract

Autoscaling microservice-based applications to satisfy Service Level Objectives (SLOs) remains challenging due to bursty workloads, cascading latency across service dependencies, and cold-start overhead. Existing approaches such as the Kubernetes Horizontal Pod Autoscaler (HPA) rely on threshold-based CPU or memory metrics, which react too slowly to traffic spikes. Recent predictive methods improve responsiveness but generate point forecasts that ignore prediction uncertainty, leading to over-provisioning or oscillatory scaling. We propose SLO-Scaler, an uncertainty-aware autoscaling framework that predicts short-horizon request rates, tail latency, and SLO violation probability using a Bayesian LSTM model. SLO-Scaler integrates confidence-interval-based scaling decisions with a dependency graph analysis module that localizes bottleneck services, avoiding unnecessary whole-chain scaling. We evaluate SLO-Scaler on the DeathStarBench Social Network benchmark deployed on Kubernetes under periodic, bursty, and long-tail traffic patterns. Under bursty traffic, SLO-Scaler reduces the SLO violation rate by 29-56%, lowers the average replica count by 18-33%, and decreases scaling event frequency by 38-59% compared with the baselines, while achieving lower tail latency.

Problem

• SLOs
• Autoscaling
• Microservices
• Bursty Workloads
• Uncertainty

Innovation

• Uncertainty-Aware
• Bayesian LSTM
• SLO-Driven Autoscaling
• Confidence-Interval-Based Decisions
• Dependency Graph Analysis