This paper proposes a context-driven federated learning system for edge-cloud urban environments. By adapting to traffic and device mobility patterns, it improves training efficiency while preserving data privacy across decentralized smart city nodes.

Isaiah Vincent Hart, Lin Mei Rong, Arvind Surya Venkatram, Ruby Noelle Foster, Juan Pablo Herrera

This research introduces adaptive genetic agents for resilient resource allocation in multi-cloud systems. The model evolves in real time based on fault patterns and availability metrics, ensuring uninterrupted service delivery across geographically distributed nodes.

Malachi Ethan Lyons, Zhou Liang Jie, Siddharth Varun Narayan, Georgia Renee Harper, Tomas Alejandro Morales

This paper presents a serverless approach for deploying AI pipelines in cloud-native environments. It eliminates container dependencies, allowing for rapid scaling and cost efficiency in real-time applications through event-triggered code execution models.

Gideon Lewis Barton, Li Yue Xin, Manav Tejas Ahuja, Isla Faith Henderson, Bruno Sebastian Vidal

This study introduces a predictive scheduling mechanism that models thermal load of virtual machines to prevent overheating in cloud data centers. The system anticipates spikes and reallocates VMs dynamically for optimal temperature control and performance stability.

Dominic Xavier Grant, Cheng Wei An, Raghav Ishaan Bansal, Clara Louise Bennett, Francisco Javier Ortega

This paper proposes a cost-aware workload management strategy for hybrid cloud environments. By detecting workload bursts and simulating pricing tiers, the model guides allocation decisions that minimize costs without sacrificing response times or SLA compliance.

Raphael Owen Hayes, Fang Zhi Cheng, Tejas Nikhil Raman, Alice Miranda Shaw, Gabriel Emilio Contreras

This research proposes a federation model for IoT data between edge and cloud nodes using latency clustering. It leverages multicast-based aggregation to optimize bandwidth and responsiveness for time-sensitive applications.

Bennett Elijah Holt, Zhao Wen Jie, Kunal Harsh Thakkar, Lydia Grace Foster, Cristian Luis Salazar

This study presents a blockchain-based model for managing access to decentralized cloud storage. Using time-locked smart contracts, the system ensures secure, revocable data permissions across multiple stakeholders while maintaining traceability and tamper resistance.

Quentin Isaac Monroe, Huang Zi Xuan, Tanmay Arvind Deshmukh, Olivia Paige Martin, Esteban Roberto Delgado

This paper introduces a stream processing framework that adapts partitioning strategies based on failure prediction metrics. It enhances resilience and throughput in cloud analytics pipelines through intelligent backup scheduling and low-latency failover routing.

Silas Damian Pierce, Zhang Hua Ling, Varun Sandeep Jain, Amelia Brooke Carter, Rafael Ignacio Munoz