# FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices

arXiv: https://arxiv.org/abs/2008.08509 · HTML: https://mlsanity.com/paper/2008.08509/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2020.

## Built on by (most cited first, 30 of 41)

- [Queue management for slo-oriented large language model serving](https://mlsanity.com/paper/2407.00047/) (2024)
- [A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud](https://mlsanity.com/paper/2205.15795/) (2022)
- [Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservices](https://mlsanity.com/paper/2212.12180/) (2022)
- [Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insight](https://mlsanity.com/paper/2407.08694/) (2024)
- [Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directions](https://mlsanity.com/paper/2106.12739/) (2021)
- [ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Serving](https://mlsanity.com/paper/2407.09486/) (2024)
- [Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems](https://mlsanity.com/paper/2503.03274/) (2025)
- [Full Scaling Automation for Sustainable Development of Green Data Centers](https://mlsanity.com/paper/2305.00706/) (2023)
- [End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliation](https://mlsanity.com/paper/2212.13706/) (2022)
- [Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNN](https://mlsanity.com/paper/2209.02551/) (2022)
- [Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecasting](https://mlsanity.com/paper/2407.19697/) (2024)
- [When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Control](https://mlsanity.com/paper/2605.26418/) (2026)
- [BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statistics](https://mlsanity.com/paper/2102.10837/) (2021)
- [PROMPT: Learning Dynamic Resource Allocation Policies for Network Applications](https://mlsanity.com/paper/2201.07916/) (2022)
- [OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud](https://mlsanity.com/paper/2311.12864/) (2023)
- [On a Foundation Model for Operating Systems](https://mlsanity.com/paper/2312.07813/) (2023)
- [Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Making](https://mlsanity.com/paper/2408.01000/) (2024)
- [Large Language Models as Realistic Microservice Trace Generators](https://mlsanity.com/paper/2502.17439/) (2024)
- [Towards Agentic OS: An LLM Agent Framework for Linux Schedulers](https://mlsanity.com/paper/2509.01245/) (2025)
- [A Learning-based Approach Towards Automated Tuning of SSD Configurations](https://mlsanity.com/paper/2110.08685/) (2021)
- [AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Study](https://mlsanity.com/paper/2305.16092/) (2023)
- [Chatting with Logs: An exploratory study on Finetuning LLMs for LogQL](https://mlsanity.com/paper/2412.03612/) (2024)
- [Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.20507/) (2025)
- [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)
- [RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments](https://mlsanity.com/paper/2304.04797/) (2023)
- [Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservices](https://mlsanity.com/paper/2304.07941/) (2023)
- [A Framework for dynamically meeting performance objectives on a service mesh](https://mlsanity.com/paper/2306.14178/) (2023)
- [Analytically-Driven Resource Management for Cloud-Native Microservices](https://mlsanity.com/paper/2401.02920/) (2024)
- [Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approach](https://mlsanity.com/paper/2403.07927/) (2024)

## Abstract (arXiv)

Modern user-facing latency-sensitive web services include numerous distributed, intercommunicating microservices that promise to simplify software development and operation. However, multiplexing of compute resources across microservices is still challenging in production because contention for shared resources can cause latency spikes that violate the service-level objectives (SLOs) of user requests. This paper presents FIRM, an intelligent fine-grained resource management framework for predictable sharing of resources across microservices to drive up overall utilization. FIRM leverages online telemetry data and machine-learning methods to adaptively (a) detect/localize microservices that cause SLO violations, (b) identify low-level resources in contention, and (c) take actions to mitigate SLO violations via dynamic reprovisioning. Experiments across four microservice benchmarks demonstrate that FIRM reduces SLO violations by up to 16x while reducing the overall requested CPU limit by up to 62%. Moreover, FIRM improves performance predictability by reducing tail latencies by up to 11x.