ARGOS, the Adaptive Reinforcement Learning-Driven Governance for Orchestrated Services, an end-to-end controller that formulates multidimensional elasticity as a per-request Markov decision process over analytics quality and cluster pressure, supported by capacity-aware admission is presented.
Javier Mateos-Bravo, S. Laso, Juan Luis Herrera et al.· 0 citations
A structured taxonomy of performance metrics for DCCS is presented, which organizes metrics into computing-level, network-level, and application/user-level categories, while also highlighting emerging dimensions such as sustainability, observability, adaptability, data locality, migration awareness, and continuum fragm...
Praveen Kumar Donta, Boris Sedlak, Alfreds Lapkovskis et al.· arXiv.org· 0 citations
Inspection of critical infrastructure, such as power lines, is increasingly conducted using unmanned aerial vehicles (UAVs) that capture aerial video for subsequent human review. Although recent edge-based approaches deploy onboard object detectors to identify predefined defect classes, these pipelines remain closed-se...
Reza Farahani, Zoha Azimi, Ilir Murturi et al.· International Conference on...· 1 citation
It is shown that the structure of service-dependency graphs, modelled as DAGs of compute stages, is a primary determinant of whether decentralised, price-based resource allocation works reliably at scale.
Lauri Lovén, Alaa Saleh, Reza Farahani et al.· arXiv.org· 2 citations
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