Cloud Computing
Cloud computing provides on-demand access to shared computing resources, aiming to optimize resource utilization, enhance scalability, and improve efficiency across diverse applications. Current research focuses on improving resource allocation through machine learning algorithms (e.g., reinforcement learning, convolutional neural networks, and gradient boosting) and addressing security and privacy concerns using techniques like multi-party computation and data obfuscation. These advancements are significantly impacting various fields, from optimizing agricultural practices through remote sensing data analysis to enabling secure and efficient training of complex machine learning models, including quantum machine learning, in a distributed manner.
Papers
Deep Learning for Sea Surface Temperature Reconstruction under Cloud Occlusion
Andrea Asperti, Ali Aydogdu, Emanuela Clementi, Angelo Greco, Lorenzo Mentaschi, Fabio Merizzi, Pietro Miraglio, Paolo Oddo, Nadia Pinardi, Alessandro Testa
Seamless Optical Cloud Computing across Edge-Metro Network for Generative AI
Sizhe Xing, Aolong Sun, Chengxi Wang, Yizhi Wang, Boyu Dong, Junhui Hu, Xuyu Deng, An Yan, Yingjun Liu, Fangchen Hu, Zhongya Li, Ouhan Huang, Junhao Zhao, Yingjun Zhou, Ziwei Li, Jianyang Shi, Xi Xiao, Richard Penty, Qixiang Cheng, Nan Chi, Junwen Zhang
Managing Bandwidth: The Key to Cloud-Assisted Autonomous Driving
Alexander Krentsel, Peter Schafhalter, Joseph E. Gonzalez, Sylvia Ratnasamy, Scott Shenker, Ion Stoica
Final Report for CHESS: Cloud, High-Performance Computing, and Edge for Science and Security
Nathan Tallent, Jan Strube, Luanzheng Guo, Hyungro Lee, Jesun Firoz, Sayan Ghosh, Bo Fang, Oceane Bel, Steven Spurgeon, Sarah Akers, Christina Doty, Erol Cromwell
AI-Driven Innovations in Modern Cloud Computing
Animesh Kumar