This paper presents a predictive load balancing framework for distributed cloud platforms. Using deep learning-based forecasting and real-time anomaly detection, the system ensures optimal traffic distribution, mitigates overload risks, and guarantees high service availability.

Edward Montgomery Graves, Lucia Margaret Thomason, Christopher Alan Burke, Danielle Irene Swanson, Hugo Franklin Merritt

We propose an optimization technique for serverless workflows in hybrid cloud architectures. By dynamically rewriting task DAGs and minimizing latency metrics, it improves execution time, balances costs, and reduces energy consumption in edge-fog-cloud computational continuums.

Fiona Jacqueline Prescott, Gabriel Louis Broderick, Helena Catherine Vaughn, Marcus Julian Connors, Elise Marianne Bradford

This research introduces a decentralized cloud security model for federated environments. Blockchain-based trust anchors and cryptographic proofs facilitate threat intelligence sharing across autonomous clouds while preserving confidentiality and ensuring data provenance integrity.

Vincent Oliver Hawthorne, Clara Yvette Sheridan, Matthew Isaiah Pritchard, Nora Celeste Hargrove, Dominic Elias Whitaker

This study proposes a 3D rendering platform utilizing cloud-based containerized GPU access and edge-level caching. The architecture reduces latency, enhances scalability, and supports real-time rendering for immersive applications in AR, VR, and simulation environments.

Harrison Jude Caldwell, Olivia Renee Granger, Tobias Malcolm Hensley, Evelyn Noemi Whitmore, Leonard Zachary Flint

We propose an adaptive AI-based compression algorithm for cloud-edge networks within smart cities. It significantly reduces bandwidth demands while retaining essential information, ensuring fast, energy-efficient, and privacy-aware data transfer across traffic, energy, and surveillance systems.

Julian Patrick McKenna, Beatrice Dawn Hawley, Felix Emmanuel Chambers, Audrey Lenore Whitaker, Sebastian Otto Greaves

This paper introduces a reinforcement learning-based auto-scaling model for multi-tenant cloud platforms. It leverages semantic metrics and contextual insights to dynamically adapt resource allocation, improving system responsiveness, energy efficiency, and tenant satisfaction in fluctuating workload scenarios.

Gabrielle Fiona Sinclair, Adrian Maurice Lowe, Eleanor Daphne Pennington, Quentin Elias Monroe, Malcolm Everett Hayes

This research develops a framework for identity federation in multi-cloud ecosystems. It addresses cross-domain authentication using cryptographic tokens and policy-driven access control, enhancing interoperability while securing user identities across distributed service providers and deployment zones.

Theodore Isaac Whitman, Natalie Elise Harding, Jasper Leon Wallace, Felicity Grace Winthrop, Bruce Franklin Norris