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Configuration Tuning for Distributed IoT Message Systems Using Deep Reinforcement Learning: Poster Abstract.

, , , , and . IoTDI, page 273-274. ACM, (2021)

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CloudCAMP: Automating Cloud Services Deployment and Management., , , and . CoRR, (2019)Linearize, predict and place: minimizing the makespan for edge-based stream processing of directed acyclic graphs., , , , , , , and . SEC, page 1-14. ACM, (2019)FECBench: An Extensible Framework for Pinpointing Sources of Performance Interference in the Cloud-Edge Resource Spectrum., , , , , and . SEC, page 331-333. IEEE, (2018)A Classification Framework for IoT Network Traffic Data for Provisioning 5G Network Slices in Smart Computing Applications., , , , , , and . SMARTCOMP, page 133-140. IEEE, (2023)Towards High-Performance Data Loading in Cloud-Native Deep Learning Systems., , , , and . COMSNETS, page 361-369. IEEE, (2024)DMSConfig: Automated Configuration Tuning for Distributed IoT Message Systems Using Deep Reinforcement Learning., , , , and . CoRR, (2023)A Model-Driven Approach to Automate the Deployment and Management of Cloud Services., , , and . UCC Companion, page 109-114. IEEE, (2018)Integrated Technique for Data Transmission Reliability in Metal Fill Monitoring Using Wireless Sensor Network., , and . ITNG, page 903-907. IEEE Computer Society, (2010)(WIP) CloudCAMP: Automating the Deployment and Management of Cloud Services., , , and . SCC, page 237-240. IEEE, (2018)Dataset Placement and Data Loading Optimizations for Cloud-Native Deep Learning Workloads., , , , and . ISORC, page 107-116. IEEE, (2023)