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
Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer
Shubham Agarwal, Sarthak Chakraborty, Shaddy Garg, Sumit Bisht, Chahat Jain, Ashritha Gonuguntla, Shiv Saini
LatticeGen: A Cooperative Framework which Hides Generated Text in a Lattice for Privacy-Aware Generation on Cloud
Mengke Zhang, Tianxing He, Tianle Wang, Lu Mi, Fatemehsadat Mireshghallah, Binyi Chen, Hao Wang, Yulia Tsvetkov
A Competition-based Pricing Strategy in Cloud Markets using Regret Minimization Techniques
S. Ghasemi, M. R. Meybodi, M. Dehghan, A. M. Rahmani
A Cost-Aware Mechanism for Optimized Resource Provisioning in Cloud Computing
Safiye Ghasemi, Mohammad Reza Meybodi, Mehdi Dehghan Takht Fooladi, Amir Masoud Rahmani