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Adaptive Control of Data Center Cooling using Deep Reinforcement Learning.

, , , , и . ACSOS-C, стр. 1-6. IEEE, (2022)

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Adaptive Control of Data Center Cooling using Deep Reinforcement Learning., , , , и . ACSOS-C, стр. 1-6. IEEE, (2022)Achieving Predictable and Low End-to-End Latency for a Network of Smart Services., , и . GLOBECOM, стр. 1-7. IEEE, (2018)A Deep Learning Approach for Root Cause Analysis in Real-Time IIoT Edge Networks., , , , , и . NOMS, стр. 1-5. IEEE, (2023)Feedback scheduling of control tasks., и . CDC, стр. 4871-4876. IEEE, (2000)An introduction to control and scheduling co-design., , , и . CDC, стр. 4865-4870. IEEE, (2000)Towards a Holistic Controller: Reinforcement Learning for Data Center Control., , , , и . e-Energy, стр. 424-429. ACM, (2021)Multicore Scheduling Issues in Ericsson Mobile Platforms.. ICPP Workshops, стр. 1. IEEE Computer Society, (2009)Cloud-Assisted Model Predictive Control., , , и . EDGE, стр. 110-112. IEEE, (2019)End-To-End Deadlines over Dynamic Topologies., , и . ECRTS, том 133 из LIPIcs, стр. 10:1-10:22. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2019)A Deep Learning Approach for Real-Time Application-Level Anomaly Detection in IoT Data Streaming., , , , , и . CCNC, стр. 449-454. IEEE, (2023)