# SLA-MORL: SLA-Aware Multi-Objective Reinforcement Learning for HPC Resource Optimization

arXiv: https://arxiv.org/abs/2508.03509 · HTML: https://mlsanity.com/paper/2508.03509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 5 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [RL for energy, networks and operations](https://mlsanity.com/deeprl/rl-for-energy-networks-and-operations/)

## Builds on (most cited first, 12 of 12)

- [Efficient Online Reinforcement Learning with Offline Data](https://mlsanity.com/paper/2302.02948/) (2023)
- [When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2206.13464/) (2022)
- [Adaptive Policy Learning for Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2303.07693/) (2023)
- [Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning](https://mlsanity.com/paper/2310.17966/) (2023)
- [Pareto Set Learning for Multi-Objective Reinforcement Learning](https://mlsanity.com/paper/2501.06773/) (2025)
- [A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning](https://mlsanity.com/paper/1703.04221/) (2017)
- [Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization](https://mlsanity.com/paper/2412.07639/) (2024)
- [gWaveNet: Classification of Gravity Waves from Noisy Satellite Data using Custom Kernel Integrated Deep Learning Method](https://mlsanity.com/paper/2408.14674/) (2024)
- [Deep Reinforcement Learning for Job Scheduling and Resource Management in Cloud Computing: An Algorithm-Level Review](https://mlsanity.com/paper/2501.01007/) (2025)
- [A HPC Co-Scheduler with Reinforcement Learning](https://mlsanity.com/paper/2401.09706/) (2024)
- [Joint Retrieval of Cloud properties using Attention-based Deep Learning Models](https://mlsanity.com/paper/2504.03133/) (2025)
- [MARS: Malleable Actor-Critic Reinforcement Learning Scheduler](https://mlsanity.com/paper/2005.01584/) (2020)

## Abstract (arXiv)

Dynamic resource allocation for machine learning workloads in cloud environments remains challenging due to competing objectives of minimizing training time and operational costs while meeting Service Level Agreement (SLA) constraints. Traditional approaches employ static resource allocation or single-objective optimization, leading to either SLA violations or resource waste. We present SLA-MORL, an adaptive multi-objective reinforcement learning framework that intelligently allocates GPU and CPU resources based on user-defined preferences (time, cost, or balanced) while ensuring SLA compliance. Our approach introduces two key innovations: (1) intelligent initialization through historical learning or efficient baseline runs that eliminates cold-start problems, reducing initial exploration overhead by 60%, and (2) dynamic weight adaptation that automatically adjusts optimization priorities based on real-time SLA violation severity, creating a self-correcting system. SLA-MORL constructs a 21-dimensional state representation capturing resource utilization, training progress, and SLA compliance, enabling an actor-critic network to make informed allocation decisions across 9 possible actions. Extensive evaluation on 13 diverse ML workloads using production HPC infrastructure demonstrates that SLA-MORL achieves 67.2% reduction in training time for deadline-critical jobs, 68.8% reduction in costs for budget-constrained workloads, and 73.4% improvement in overall SLA compliance compared to static baselines. By addressing both cold-start inefficiency and dynamic adaptation challenges, SLA-MORL provides a practical solution for cloud resource management that balances performance, cost, and reliability in modern ML training environments.