This paper introduces a microservice deployment method optimized for latency-sensitive applications. A reinforced placement algorithm evaluates performance constraints and traffic patterns to achieve efficient service distribution across geographically dispersed cloud environments.

Garrett Maxwell Singh, Liu Xiao Fen, Meenakshi Sushma Ranganathan, Delilah June Patterson, Tomasz Adrian Nowak

This study explores AI-based profiling methods to predict workload fluctuations in hybrid cloud-edge ecosystems. The model enables proactive elasticity by forecasting future demand patterns and allocating resources to maintain performance and cost efficiency.

Vincent Harold Beasley, Zheng Rui Qing, Priyanka Shyam Narayanan, Amelia Brooke Saunders, Marco Alejandro Vidal

This research introduces a secure migration framework for legacy workloads transitioning to cloud environments. Zero trust architecture and fine-grained policy segmentation ensure confidentiality and controlled access throughout the migration and post-deployment lifecycle.

Raymond Oliver Hughes, Sun Wei Lin, Abhinav Rajkumar Nair, Paige Elise Chambers, Andre Felipe Morales

This paper introduces a federated learning system enabling secure medical model training across cloud-connected hospitals. Patient data remains decentralized while global models improve through collaborative updates, enhancing diagnostic accuracy and safeguarding privacy.

Theodore James Randall, Zhang Yan Xin, Ruchika Balaji Sharma, Eleanor Joy Cunningham, Nicolas Arturo Reyes

This study presents an SLA management framework for containerized applications in multi-cloud setups. Predictive performance metrics and service adaptation techniques ensure SLA compliance by proactively identifying and mitigating potential breaches across cloud regions.

Douglas Ian Merrick, Liu Mei Zhen, Aarav Suresh Patankar, Isla Violet Griffin, Bruno Leonardo Espinoza

This research introduces a cloud-native orchestration platform for digital twins using scalable, event-driven microservices. The system enables real-time synchronization and optimization of industrial processes, reducing latency and maximizing productivity across distributed infrastructures.

Wyatt Jordan McAllister, Chen Yue Feng, Shubham Rakesh Tripathi, Fiona Claire Donnelly, Matteo Antonio Ruiz

This study proposes an adaptive load balancing framework that uses real-time traffic analytics and session profiling to ensure optimal distribution across cloud servers. The system increases throughput, minimizes latency, and scales elastically during peak demand periods.

Xander Tobias Greene, Wang Li Mei, Kavya Deepan Ramani, Alice Johanna Blackwell, Eduardo Miguel Rojas