Summary of Adaptive Stream Processing on Edge Devices Through Active Inference, by Boris Sedlak et al.
Adaptive Stream Processing on Edge Devices through Active Inference
by Boris Sedlak, Victor Casamayor Pujol, Andrea Morichetta, Praveen Kumar Donta, Schahram Dustdar
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel Machine Learning (ML) paradigm based on Active Inference (AIF), which is inspired by the way the brain processes sensory information. The AIF-based agent continuously optimizes the fulfillment of Service Level Objectives (SLOs) for three autonomous driving services running on multiple devices, leveraging causal knowledge to develop an understanding of its actions’ impact on requirements fulfillment. This approach requires up to thirty iterations to converge to the optimal solution and provides accurate results in a short amount of time. Furthermore, AIF’s causal structures ensure transparency in decision-making, making result interpretation and troubleshooting effortless. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for processing data generated by IoT devices by using Machine Learning (ML) based on Active Inference (AIF). This approach helps to predict and control the data processing better. The researchers implemented this idea and tested it with autonomous driving services running on multiple devices, showing that it can provide accurate results in just a few iterations. |
Keywords
» Artificial intelligence » Inference » Machine learning
