This paper explores privacy-aware resource allocation frameworks for distributed cloud environments. We integrate regulatory compliance rules within machine learning-based allocation models to ensure secure processing of sensitive data across jurisdictions, optimizing both legal adherence and system performance.

George Andrew Kessler, Olivia Jane Bartlett, Martin Douglas Crowley, Fiona Isabel Hawthorne, Vincent Charles Winthrop

We introduce a predictive analytics framework that forecasts workload demands and redistributes cloud tasks accordingly to optimize energy efficiency. By modeling historical usage patterns, the system enhances green computing standards in high-demand distributed environments.

Christopher Dean Ellsworth, Helena Margaret Dobson, Arthur Maxwell Spence, Lucy Ann Kingsley, Malcolm Henry Whitaker

This research presents a strategy for seamless container migration across heterogeneous cloud platforms using dynamic orchestration of service mesh policies. The framework ensures fault tolerance, service continuity, and governance alignment during runtime application relocations.

Isabelle Nora Clay, Patrick John Denholm, Matilda Elise Browning, Edward Francis Whitmore, Charlotte Rose Halford

We propose a fault-tolerant storage allocation model tailored for blockchain systems operating in hybrid cloud infrastructures. Our method ensures integrity and redundancy of ledger data while balancing load across cloud-native and edge components in dynamic network conditions.

Rupert Elias Townsend, Amelia Florence Yates, Gregory Paul Hargrove, Mathew Isaac Colton, Eliza Catherine Windsor

This paper introduces a queue-aware scheduling mechanism for managing real-time data flows in complex cloud pipelines. It dynamically reprioritizes tasks based on load, latency, and service-level requirements, minimizing pipeline stalling and improving end-to-end processing throughput.

Julian Benedict Arkwright, Samantha Grace Hollings, Oliver Benedict Radford, Theresa Diane Wainwright, Henry Jacob Milton

This paper proposes a policy-based dynamic cloud federation framework for processing real-time IoT data streams. By aligning service provisioning with regional policies and latency constraints, the system ensures optimal data throughput and compliance across cross-border federated cloud environments.

Lawrence Ethan Crenshaw, Abigail Sophie Westwood, Dominic Harold Kingsley, Rebecca Jane Templeton, Marcus Edwin Holloway

We present a federated learning-based framework for latency-aware replication of microservices in distributed clouds. The system learns user access patterns in real time and predicts optimal replication strategies to reduce service delay while balancing cost and resource consumption.

Emily Frances Whitcomb, Joshua Nathaniel Blackwood, Chloe Miranda Larkspur, Frederick Alan Whitaker, Natalie Rose McKinley