Adaptive stream processing on edge devices through active inference

The current scenario of IoT is witnessing a constant increase on the volume of data, which is generated in constant stream, calling for novel architectural and logical solutions for processing it. Moving the data handling towards the edge of the computing spectrum guarantees better distribution of l...

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Vydáno v:Evolving systems Ročník 16; číslo 4; s. 130
Hlavní autoři: Sedlak, Boris, Casamayor Pujol, Victor, Morichetta, Andrea, Donta, Praveen Kumar, Dustdar, Schahram
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2025
Springer Nature B.V
Témata:
Active Inference
Artificial Intelligence
Complex Systems
Complexity
Computer and Systems Sciences
data- och systemvetenskap
Edge Intelligence
Engineering
Inference
Machine Learning
Markov Blanket
Neurosciences
Original Paper
Quality of service
Service Level Objectives
Troubleshooting
Markov blanket
Active inference
Service level objectives
Machine learning
Edge intelligence
ISSN:1868-6478, 1868-6486, 1868-6486
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Shrnutí:The current scenario of IoT is witnessing a constant increase on the volume of data, which is generated in constant stream, calling for novel architectural and logical solutions for processing it. Moving the data handling towards the edge of the computing spectrum guarantees better distribution of load and, in principle, lower latency and better privacy. However, managing such a structure is complex, especially when requirements, also referred to Service Level Objectives (SLOs), specified by applications’ owners and infrastructure managers need to be ensured. Despite the rich number of proposals of Machine Learning (ML) based management solutions, researchers and practitioners yet struggle to guarantee long-term prediction and control, and accurate troubleshooting. Therefore, we present a novel ML paradigm based on Active Inference (AIF)—a concept from neuroscience that describes how the brain constantly predicts and evaluates sensory information to decrease long-term surprise. We implement it and evaluate it in a heterogeneous real stream processing use case, where an AIF-based agent continuously optimizes the fulfillment of three SLOs for three autonomous driving services running on multiple devices. The agent used causal knowledge to gradually develop an understanding of how its actions are related to requirements fulfillment, and which configurations to favor. Through this approach, our agent requires up to thirty iterations to converge to the optimal solution, showing the capability of offering accurate results in a short amount of time. Furthermore, thanks to AIF and its causal structures, our method guarantees full transparency on the decision making, making the interpretation of the results and the troubleshooting effortless.
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ISSN:1868-6478
1868-6486
1868-6486
DOI:10.1007/s12530-025-09753-2