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Leveraging User Mobility and Mobile App Services Behavior for Optimal Edge Resource Utilization.

, , , , and . COINS, page 7-12. ACM, (2019)

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Task offloading in Edge and Cloud Computing: A survey on mathematical, artificial intelligence and control theory solutions., , , , , , , , , and . Comput. Networks, (2021)Mapping of Quality of Service Requirements to Resource Demands for IaaS., , , , , , , , and . CLOSER, page 263-270. SciTePress, (2019)Hypertuming GRU Neural Networks for Edge Resource Usage Prediction., , , , , and . ISCC, page 1-8. IEEE, (2021)Automated and Reproducible Application Traces Generation for IoT Applications., , , , , , and . Q2SWinet, page 17-24. ACM, (2021)Enhancing Virtual Sensors to deal with Missing Values and Low Sampling Rates., , , , and . MobileCloud, page 39-44. IEEE, (2023)Sentiment Analysis using Word-Graphs., , , , and . WIMS, page 22:1-22:9. ACM, (2016)Toward QoS Prediction Based on Temporal Transformers for IoT Applications., , , , , and . IEEE Trans. Netw. Serv. Manag., 19 (4): 4010-4027 (December 2022)User Association and Behavioral Characterization during Task Offloading at the Edge., , , , , and . MeditCom, page 70-75. IEEE, (2021)Intelligent Horizontal Autoscaling in Edge Computing using a Double Tower Neural Network., , , , , and . Comput. Networks, (2022)Multi-Agent Deep Reinforcement Learning for Weighted Multi-Path Routing., , , , and . FRAME@HPDC, page 7-11. ACM, (2023)