# Microservice Deployment in Space Computing Power Networks via Robust Reinforcement Learning

arXiv: https://arxiv.org/abs/2501.06244 · HTML: https://mlsanity.com/paper/2501.06244/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 23 of its 53 references that are in the map.
- First posted on arXiv 8 Jan 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 23 of 23)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Robust Adversarial Reinforcement Learning](https://mlsanity.com/paper/1703.02702/) (2017)
- [Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations](https://mlsanity.com/paper/2003.08938/) (2020)
- [Reinforcement Learning for Combinatorial Optimization: A Survey](https://mlsanity.com/paper/2003.03600/) (2020)
- [EarthGPT: A Universal Multi-modal Large Language Model for Multi-sensor Image Comprehension in Remote Sensing Domain](https://mlsanity.com/paper/2401.16822/) (2024)
- [The Roadmap to 6G -- AI Empowered Wireless Networks](https://mlsanity.com/paper/1904.11686/) (2019)
- [RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2204.12581/) (2022)
- [Online Robust Reinforcement Learning with Model Uncertainty](https://mlsanity.com/paper/2109.14523/) (2021)
- [Policy Gradient Method For Robust Reinforcement Learning](https://mlsanity.com/paper/2205.07344/) (2022)
- [Policy Gradient in Robust MDPs with Global Convergence Guarantee](https://mlsanity.com/paper/2212.10439/) (2022)
- [Robust Multi-Agent Reinforcement Learning with State Uncertainty](https://mlsanity.com/paper/2307.16212/) (2023)
- [Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness](https://mlsanity.com/paper/2301.07487/) (2023)
- [Investigating Vulnerabilities of Deep Neural Policies](https://mlsanity.com/paper/2108.13093/) (2021)
- [Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs](https://mlsanity.com/paper/2112.09025/) (2021)
- [Satellite edge computing for real-time and very-high resolution Earth observation](https://mlsanity.com/paper/2212.12912/) (2022)
- [Satellite Federated Edge Learning: Architecture Design and Convergence Analysis](https://mlsanity.com/paper/2404.01875/) (2024)
- [Understanding and Diagnosing Deep Reinforcement Learning](https://mlsanity.com/paper/2406.16979/) (2024)
- [Online Deployment Algorithms for Microservice Systems with Complex Dependencies](https://mlsanity.com/paper/2107.09308/) (2021)
- [Computing-Aware Routing for LEO Satellite Networks: A Transmission and Computation Integration Approach](https://mlsanity.com/paper/2211.08820/) (2022)
- [On-board Change Detection for Resource-efficient Earth Observation with LEO Satellites](https://mlsanity.com/paper/2305.10119/) (2023)
- [Two-Stage Robust Edge Service Placement and Sizing under Demand Uncertainty](https://mlsanity.com/paper/2004.13218/) (2020)
- [Delay-Aware Robust Edge Network Hardening Under Decision-Dependent Uncertainty](https://mlsanity.com/paper/2407.06142/) (2024)
- [Hierarchical Learning and Computing over Space-Ground Integrated Networks](https://mlsanity.com/paper/2408.14116/) (2024)

## Built on by (most cited first, 1 of 1)

- [GNN-enhanced Traffic Anomaly Detection for Next-Generation SDN-Enabled Consumer Electronics](https://mlsanity.com/paper/2510.07109/) (2025)

## Abstract (arXiv)

With the growing demand for Earth observation, it is important to provide reliable real-time remote sensing inference services to meet the low-latency requirements. The Space Computing Power Network (Space-CPN) offers a promising solution by providing onboard computing and extensive coverage capabilities for real-time inference. This paper presents a remote sensing artificial intelligence applications deployment framework designed for Low Earth Orbit satellite constellations to achieve real-time inference performance. The framework employs the microservice architecture, decomposing monolithic inference tasks into reusable, independent modules to address high latency and resource heterogeneity. This distributed approach enables optimized microservice deployment, minimizing resource utilization while meeting quality of service and functional requirements. We introduce Robust Optimization to the deployment problem to address data uncertainty. Additionally, we model the Robust Optimization problem as a Partially Observable Markov Decision Process and propose a robust reinforcement learning algorithm to handle the semi-infinite Quality of Service constraints. Our approach yields sub-optimal solutions that minimize accuracy loss while maintaining acceptable computational costs. Simulation results demonstrate the effectiveness of our framework.