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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| Veröffentlicht in: | Evolving systems Jg. 16; H. 4; S. 130 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.12.2025
Springer Nature B.V |
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| ISSN: | 1868-6478, 1868-6486, 1868-6486 |
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| Abstract | 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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| AbstractList | 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. |
| ArticleNumber | 130 |
| Author | Dustdar, Schahram Donta, Praveen Kumar Sedlak, Boris Casamayor Pujol, Victor Morichetta, Andrea |
| Author_xml | – sequence: 1 givenname: Boris orcidid: 0009-0001-2365-8265 surname: Sedlak fullname: Sedlak, Boris email: b.sedlak@dsg.tuwien.ac.at organization: Distributed Systems Group, TU Wien – sequence: 2 givenname: Victor orcidid: 0000-0003-2830-8368 surname: Casamayor Pujol fullname: Casamayor Pujol, Victor organization: Distributed Intelligence and Systems-Engineering Lab (DISL), Universitat Pompeu Fabra – sequence: 3 givenname: Andrea surname: Morichetta fullname: Morichetta, Andrea organization: Distributed Systems Group, TU Wien – sequence: 4 givenname: Praveen Kumar orcidid: 0000-0002-8233-6071 surname: Donta fullname: Donta, Praveen Kumar organization: Department of Computer and Systems Sciences, Stockholm University – sequence: 5 givenname: Schahram orcidid: 0000-0001-6872-8821 surname: Dustdar fullname: Dustdar, Schahram organization: Distributed Systems Group, TU Wien, Distributed Intelligence and Systems-Engineering Lab (DISL), Universitat Pompeu Fabra |
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| SubjectTerms | 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 |
| Title | Adaptive stream processing on edge devices through active inference |
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