# Benchmarking Dynamic SLO Compliance in Distributed Computing Continuum Systems

arXiv: https://arxiv.org/abs/2503.03274 · HTML: https://mlsanity.com/paper/2503.03274/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 38 references that are in the map.
- First posted on arXiv 5 Mar 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, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [FIRM: An Intelligent Fine-Grained Resource Management Framework for SLO-Oriented Microservices](https://mlsanity.com/paper/2008.08509/) (2020)
- [Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestration](https://mlsanity.com/paper/2205.01423/) (2022)
- [Equilibrium in the Computing Continuum through Active Inference](https://mlsanity.com/paper/2311.16769/) (2023)
- [Adaptive Stream Processing on Edge Devices through Active Inference](https://mlsanity.com/paper/2409.17937/) (2024)
- [Towards smart and adaptive agents for active sensing on edge devices](https://mlsanity.com/paper/2501.06262/) (2025)

## Built on by (most cited first, 6 of 6)

- [UserCentrix: An Agentic Memory-augmented AI Framework for Smart Spaces](https://mlsanity.com/paper/2505.00472/) (2025)
- [Resilient by Design -- Active Inference for Distributed Continuum Intelligence](https://mlsanity.com/paper/2511.07202/) (2025)
- [Performance Measurements in the AI-Centric Computing Continuum Systems](https://mlsanity.com/paper/2506.22884/) (2025)
- [Multi-dimensional Autoscaling of Processing Services: A Comparison of Agent-based Methods](https://mlsanity.com/paper/2506.10420/) (2025)
- [A Microservice-Based Platform for Sustainable and Intelligent SLO Fulfilment and Service Management](https://mlsanity.com/paper/2602.12875/) (2026)
- [An Uncertainty-Aware Resilience Micro-Agent for Causal Observability in the Computing Continuum](https://mlsanity.com/paper/2605.10718/) (2026)

## Abstract (arXiv)

Ensuring Service Level Objectives (SLOs) in large-scale architectures, such as Distributed Computing Continuum Systems (DCCS), is challenging due to their heterogeneous nature and varying service requirements across different devices and applications. Additionally, unpredictable workloads and resource limitations lead to fluctuating performance and violated SLOs. To improve SLO compliance in DCCS, one possibility is to apply machine learning; however, the design choices are often left to the developer. To that extent, we provide a benchmark of Active Inference -- an emerging method from neuroscience -- against three established reinforcement learning algorithms (Deep Q-Network, Advantage Actor-Critic, and Proximal Policy Optimization). We consider a realistic DCCS use case: an edge device running a video conferencing application alongside a WebSocket server streaming videos. Using one of the respective algorithms, we continuously monitor key performance metrics, such as latency and bandwidth usage, to dynamically adjust parameters -- including the number of streams, frame rate, and resolution -- to optimize service quality and user experience. To test algorithms' adaptability to constant system changes, we simulate dynamically changing SLOs and both instant and gradual data-shift scenarios, such as network bandwidth limitations and fluctuating device thermal states. Although the evaluated algorithms all showed advantages and limitations, our findings demonstrate that Active Inference is a promising approach for ensuring SLO compliance in DCCS, offering lower memory usage, stable CPU utilization, and fast convergence.