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Adaptive Control of Data Center Cooling using Deep Reinforcement Learning., , , , and . ACSOS-C, page 1-6. IEEE, (2022)Achieving Predictable and Low End-to-End Latency for a Network of Smart Services., , and . GLOBECOM, page 1-7. IEEE, (2018)End-To-End Deadlines over Dynamic Topologies., , and . ECRTS, volume 133 of LIPIcs, page 10:1-10:22. Schloss Dagstuhl - Leibniz-Zentrum für Informatik, (2019)A Deep Learning Approach for Root Cause Analysis in Real-Time IIoT Edge Networks., , , , , and . NOMS, page 1-5. IEEE, (2023)An introduction to control and scheduling co-design., , , and . CDC, page 4865-4870. IEEE, (2000)Feedback scheduling of control tasks., and . CDC, page 4871-4876. IEEE, (2000)Towards a Holistic Controller: Reinforcement Learning for Data Center Control., , , , and . e-Energy, page 424-429. ACM, (2021)Multicore Scheduling Issues in Ericsson Mobile Platforms.. ICPP Workshops, page 1. IEEE Computer Society, (2009)Cloud-Assisted Model Predictive Control., , , and . EDGE, page 110-112. IEEE, (2019)DDPC: Automated Data-Driven Power-Performance Controller Design on-the-fly for Latency-sensitive Web Services., , , , and . WWW, page 3067-3076. ACM, (2023)