This paper presents a secure data sharing mechanism leveraging blockchain to ensure traceable analytics across federated cloud environments. Smart contracts and consensus models enforce data governance while enabling distributed insights across multi-tenant data lakes.

Caleb Dominic Stratton, Li Wei Kang, Ramesh Chandra Kulshreshtha, Olivia Marie Granger, Enzo Rafael Montoya

This research introduces predictive pricing algorithms to optimize cloud costs by dynamically switching workloads across providers. The approach balances performance and budget constraints through multi-metric forecasting and responsive reallocation strategies in fluctuating billing environments.

Simon Gabriel Whitmore, Zhao Hui Long, Nishanth Vetrivel Iyengar, Chloe Danielle Brooks, Alejandro Manuel Ruiz

This paper presents a reinforcement learning-based CDN control system that reshapes delivery routes based on real-time network congestion and demand patterns. The model learns efficient routing under constraints while maintaining service quality for diverse content types.

Jasper Leo Whitman, Liu Chen Rong, Aravind Mahadevan Reddy, Grace Emilia Thornton, Matteo Javier De Luca

This work optimizes high-throughput genomics workflows in cloud environments through adaptive auto-scaling. Task-specific models predict compute needs, dynamically adjusting infrastructure for sequencing, alignment, and annotation stages to reduce execution time and operational cost.

Reuben Elias Montgomery, Zhang Wei Lian, Mitali Abhinaya Rao, Charlotte Nadine Harper, Francisco Angel Carvajal

This paper introduces an energy-aware scheduling algorithm tailored for serverless computing environments. It aligns execution of event-driven applications with energy availability and carbon intensity, promoting sustainable computing across decentralized green cloud infrastructures.

Ronald Everett Cole, Zhao Yi Min, Deepa Shanmugha Sundaram, Emma Renee Caldwell, Santiago Miguel Barrios

This research proposes a security-hardening framework for multi-tenant cloud platforms. Context-aware isolation and adaptive detection systems protect data and services from lateral threats while minimizing impact on compute performance and deployment speed in shared infrastructures.

Trevor Alistair Donovan, Yang Mei Wen, Harishkumar Anandh Velu, Clara Isabelle Whitmore, Giovanni Pablo Sánchez

This study introduces an intelligent monitoring framework for cloud-native applications using self-learning anomaly classifiers. The system ingests telemetry data in real-time, adapts to workload drift, and improves precision in detecting silent failures or service degradations.

Nigel Rupert Evans, Liu Rong Xia, Sahana Rajendran Iyer, Kaitlyn Noelle Greene, Eduardo Felipe Mendoza