mlsanityData to 7 Oct 2026

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

arXiv 2008.08509 ↗First posted 19 Aug 2020cs.DC cs.PFcited by 41

Not on the map. Why?

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.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Aug 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 3; 2022: 5; 2023: 7; 2024: 10; 2025: 5; 2026: 11.

41 papers.

Sort:

By area:

  1. 2024Queue management for slo-oriented large language model servingcited by 36
  2. 2022A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloudcited by 16
  3. 2022Autothrottle: A Practical Bi-Level Approach to Resource Management for SLO-Targeted Microservicescited by 12
  4. 2024Cloud Atlas: Efficient Fault Localization for Cloud Systems using Language Models and Causal Insightcited by 11
  5. 2021Machine Learning-based Orchestration of Containers: A Taxonomy and Future Directionscited by 7
  6. 2024ENOVA: Autoscaling towards Cost-effective and Stable Serverless LLM Servingcited by 6
  7. 2025Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systemscited by 6
  8. 2023Full Scaling Automation for Sustainable Development of Green Data Centerscited by 5
  9. 2022End-to-End Modeling Hierarchical Time Series Using Autoregressive Transformer and Conditional Normalizing Flow based Reconciliationcited by 4
  10. 2022Graph-PHPA: Graph-based Proactive Horizontal Pod Autoscaling for Microservices using LSTM-GNNcited by 3
  11. 2024Multiscale Representation Enhanced Temporal Flow Fusion Model for Long-Term Workload Forecastingcited by 3
  12. 2026When Does Deep RL Beat Calibrated Baselines? A Benchmark Study on Adaptive Resource Controlcited by 3
29 more
  1. 2021BayesPerf: Minimizing Performance Monitoring Errors Using Bayesian Statisticscited by 2
  2. 2022PROMPT: Learning Dynamic Resource Allocation Policies for Network Applicationscited by 2
  3. 2023OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloudcited by 2
  4. 2023On a Foundation Model for Operating Systemscited by 2
  5. 2024Adaptive Two-Stage Cloud Resource Scaling via Hierarchical Multi-Indicator Forecasting and Bayesian Decision-Makingcited by 2
  6. 2024Large Language Models as Realistic Microservice Trace Generatorscited by 2
  7. 2025Towards Agentic OS: An LLM Agent Framework for Linux Schedulerscited by 2
  8. 2021A Learning-based Approach Towards Automated Tuning of SSD Configurationscited by 1
  9. 2023AI Techniques in the Microservices Life-Cycle: A Systematic Mapping Studycited by 1
  10. 2024Chatting with Logs: An exploratory study on Finetuning LLMs for LogQLcited by 1
  11. 2025Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learningcited by 1
  12. 2025Multi-Dimensional Autoscaling of Stream Processing Services on Edge Devicescited by 1
  13. 2026EMA: Efficient Model Adaptation for Learning-based Systemscited by 1
  14. 2023RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environmentscited by 0
  15. 2023Reclaimer: A Reinforcement Learning Approach to Dynamic Resource Allocation for Cloud Microservicescited by 0
  16. 2023A Framework for dynamically meeting performance objectives on a service meshcited by 0
  17. 2024Analytically-Driven Resource Management for Cloud-Native Microservicescited by 0
  18. 2024Intelligent Monitoring Framework for Cloud Services: A Data-Driven Approachcited by 0
  19. 2024CONGO: Compressive Online Gradient Optimizationcited by 0
  20. 2025Is Intelligence the Right Direction in New OS Scheduling for Multiple Resources in Cloud Environments?cited by 0
  21. 2026SAIR: Cost-Efficient Multi-Stage ML Pipeline Autoscaling via In-Context Reinforcement Learningcited by 0
  22. 2026Analyzing Symbolic Properties for DRL Agents in Systems and Networkingcited by 0
  23. 2026STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservicescited by 0
  24. 2026ADAPT: A Self-Calibrating Proactive Autoscaler for Container Orchestrationcited by 0
  25. 2026Incentives and Evidence in Learned Service Orchestrationcited by 0
  26. 2026Learning Burst-Aware Early Warning Models for Capacity Stress under AI Workload Surges in Hyperscale Data Centerscited by 0
  27. 2026Auto-Scaling Heterogeneous Neural Processing Units for Energy and Cost-Efficient LLM Servingcited by 0
  28. 2026OpScale: Operator-level Provisioning and Autoscaling for LLM Servingcited by 0
  29. 2026Cross-Model Autoscaling for Shared LLM Servingcited by 0