Equilibrium in the computing continuum through active inference

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• dc.contributor.author Sedlak, Boris
• dc.contributor.author Casamayor Pujol, Víctor
• dc.contributor.author Donta, Praveen Kumar
• dc.contributor.author Dustdar, Schahram
• dc.date.accessioned 2025-10-21T05:46:38Z
• dc.date.available 2025-10-21T05:46:38Z
• dc.date.issued 2024
• dc.description.abstract Computing Continuum (CC) systems are challenged to ensure the intricate requirements of each computational tier. Given the system’s scale, the Service Level Objectives (SLOs), which are expressed as these requirements, must be disaggregated into smaller parts that can be decentralized. We present our framework for collaborative edge intelligence, enabling individual edge devices to (1) develop a causal understanding of how to enforce their SLOs and (2) transfer knowledge to speed up the onboarding of heterogeneous devices. Through collaboration, they (3) increase the scope of SLO fulfillment. We implemented the framework and evaluated a use case in which a CC system is responsible for ensuring Quality of Service (QoS) and Quality of Experience (QoE) during video streaming. Our results showed that edge devices required only ten training rounds to ensure four SLOs; furthermore, the underlying causal structures were also rationally explainable. The addition of new types of devices can be done a posteriori; the framework allowed them to reuse existing models, even though the device type had been unknown. Finally, rebalancing the load within a device cluster allowed individual edge devices to recover their SLO compliance after a network failure from 22% to 89%.en
• dc.description.sponsorship Funded by European Union (TEADAL, 101070186). Views and opinions expressed are those of the authors and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible.en
• dc.format.mimetype application/pdf
• dc.identifier.citation Sedlak B, Casamayor V, Donta PK, Dustdar S. Equilibrium in the computing continuum through active inference. Future Gener Comput Syst. 2024 Nov;160:92-108. DOI: 10.1016/j.future.2024.05.056
• dc.identifier.doi http://dx.doi.org/10.1016/j.future.2024.05.056
• dc.identifier.issn 0167-739X
• dc.identifier.uri http://hdl.handle.net/10230/71598
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof Future Generation Computer Systems. 2024 Nov;160:92-108
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/101070186
• dc.rights © 2024 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Active inferenceen
• dc.subject.keyword Computing continuumen
• dc.subject.keyword Scalabilityen
• dc.subject.keyword Edge intelligenceen
• dc.subject.keyword Transfer learningen
• dc.subject.keyword Equilibriumen
• dc.title Equilibrium in the computing continuum through active inferenceen
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion