# PROMPT: Learning Dynamic Resource Allocation Policies for Network Applications

arXiv: https://arxiv.org/abs/2201.07916 · HTML: https://mlsanity.com/paper/2201.07916/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 71 references that are in the map.
- First posted on arXiv 19 Jan 2022.

## Builds on (most cited first, 7 of 7)

- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Action Branching Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1711.08946/) (2017)
- [FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices](https://mlsanity.com/paper/2008.08509/) (2020)
- [COSCO: Container Orchestration using Co-Simulation and Gradient Based Optimization for Fog Computing Environments](https://mlsanity.com/paper/2104.14392/) (2021)
- [A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning](https://mlsanity.com/paper/1703.04221/) (2017)
- [Improving Spark Application Throughput Via Memory Aware Task Co-location: A Mixture of Experts Approach](https://mlsanity.com/paper/1710.00610/) (2017)
- [GOSH: Task Scheduling Using Deep Surrogate Models in Fog Computing Environments](https://mlsanity.com/paper/2112.08916/) (2021)

## Built on by (most cited first, 2 of 2)

- [RAPID: Enabling Fast Online Policy Learning in Dynamic Public Cloud Environments](https://mlsanity.com/paper/2304.04797/) (2023)
- [An Advantage-based Optimization Method for Reinforcement Learning in Large Action Space](https://mlsanity.com/paper/2412.12605/) (2024)

## Abstract (arXiv)

A growing number of service providers are exploring methods to improve server utilization and reduce power consumption by co-scheduling high-priority latency-critical workloads with best-effort workloads. This practice requires strict resource allocation between workloads to reduce contention and maintain Quality-of-Service (QoS) guarantees. Prior work demonstrated promising opportunities to dynamically allocate resources based on workload demand, but may fail to meet QoS objectives in more stringent operating environments due to the presence of resource allocation cliffs, transient fluctuations in workload performance, and rapidly changing resource demand. We therefore propose PROMPT, a novel resource allocation framework using proactive QoS prediction to guide a reinforcement learning controller. PROMPT enables more precise resource optimization, more consistent handling of transient behaviors, and more robust generalization when co-scheduling new best-effort workloads not encountered during policy training. Evaluation shows that the proposed method incurs 4.2x fewer QoS violations, reduces severity of QoS violations by 12.7x, improves best-effort workload performance, and improves overall power efficiency over prior work.