# Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices

arXiv: https://arxiv.org/abs/2212.12180 · HTML: https://mlsanity.com/paper/2212.12180/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 23 Dec 2022.

## Builds on (most cited first, 8 of 8)

- [Recommendations as Treatments: Debiasing Learning and Evaluation](https://mlsanity.com/paper/1602.05352/) (2016)
- [Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits](https://mlsanity.com/paper/1402.0555/) (2014)
- [Learning Scheduling Algorithms for Data Processing Clusters](https://mlsanity.com/paper/1810.01963/) (2018)
- [A Contextual Bandit Bake-off](https://mlsanity.com/paper/1802.04064/) (2018)
- [FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices](https://mlsanity.com/paper/2008.08509/) (2020)
- [Automatic Curriculum Generation for Learning Adaptation in Networking](https://mlsanity.com/paper/2202.05940/) (2022)
- [Overload Control for Scaling WeChat Microservices](https://mlsanity.com/paper/1806.04075/) (2018)
- [Collective Autoscaling for Cloud Microservices](https://mlsanity.com/paper/2112.14845/) (2021)

## Built on by (most cited first, 12 of 12)

- [Agentic Witnessing: Pragmatic and Scalable TEE-Enabled Privacy-Preserving Auditing](https://mlsanity.com/paper/2604.24203/) (2026)
- [Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devices](https://mlsanity.com/paper/2510.06882/) (2025)
- [EMA: Efficient Model Adaptation for Learning-based Systems](https://mlsanity.com/paper/2605.13942/) (2026)
- [CONGO: Compressive Online Gradient Optimization](https://mlsanity.com/paper/2407.06325/) (2024)
- [Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods](https://mlsanity.com/paper/2506.10420/) (2025)
- [A Holistic Framework for Automated Configuration Recommendation for Cloud Service Monitoring](https://mlsanity.com/paper/2603.12268/) (2026)
- [ARL-Tangram: Unleash the Resource Efficiency in Agentic Reinforcement Learning](https://mlsanity.com/paper/2603.13019/) (2026)
- [Analyzing Symbolic Properties for DRL Agents in Systems and Networking](https://mlsanity.com/paper/2604.04914/) (2026)
- [LegoDiffusion: Micro-Serving Text-to-Image Diffusion Workflows](https://mlsanity.com/paper/2604.08123/) (2026)
- [STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices](https://mlsanity.com/paper/2604.26422/) (2026)
- [TuxBot: Semantic-Aware Online OS Tuning with Large Language Models](https://mlsanity.com/paper/2605.15026/) (2026)
- [OpScale: Operator-level Provisioning and Autoscaling for LLM Serving](https://mlsanity.com/paper/2608.13499/) (2026)

## Abstract (arXiv)

Achieving resource efficiency while preserving end-user experience is non-trivial for cloud application operators. As cloud applications progressively adopt microservices, resource managers are faced with two distinct levels of system behavior: end-to-end application latency and per-service resource usage. Translating between the two levels, however, is challenging because user requests traverse heterogeneous services that collectively (but unevenly) contribute to the end-to-end latency. We present Autothrottle, a bi-level resource management framework for microservices with latency SLOs (service-level objectives). It architecturally decouples application SLO feedback from service resource control, and bridges them through the notion of performance targets. Specifically, an application-wide learning-based controller is employed to periodically set performance targets -- expressed as CPU throttle ratios -- for per-service heuristic controllers to attain. We evaluate Autothrottle on three microservice applications, with workload traces from production scenarios. Results show superior CPU savings, up to 26.21% over the best-performing baseline and up to 93.84% over all baselines.