This paper proposes a context-aware caching mechanism for cloud-assisted IoT networks. It leverages spatiotemporal patterns to predict user requests and cache content closer to the edge, significantly improving data access speed and reducing cloud load.

Gavin Elias Foster, Sun Qiao Zhen, Bhavya Krishnan Iyer, Sadie Aurora West, Leonardo Rafael Barros

The research presents an edge intelligence model supporting real-time task partitioning and migration in multi-cloud robotic ecosystems. It enables faster decision-making, reduces cloud reliance, and ensures robust robotic control across network disruptions and latency-sensitive operations.

Spencer Calvin Ross, Zhao Mei Ling, Arnav Tejas Shah, Gabriella Marie Norton, Emilio Javier Herrera

This paper introduces a graph-based anomaly prediction model for cloud-driven industrial control systems. Using machine learning over temporal event graphs, the model identifies abnormal trends before failures occur, boosting uptime and safety in remote operations.

Holden Elias Mitchell, Cheng Wan Hui, Vinay Manohar Pillai, Peyton Amelia Clarke, Andres Javier Mendoza

This study proposes a scheduling strategy tailored for AI inference workloads in containerized cloud platforms. The solution dynamically provisions resources based on model size and input frequency, supporting horizontal scalability and cost-effective inference delivery.

Dallas Aaron Reed, Bai Chen Hui, Pranav Rajesh Deshmukh, London Isabelle Moore, Tomas Luis Cardenas

This paper presents a deep reinforcement learning-based approach to compose cloud services in smart energy grids. The proposed multi-agent system adapts dynamically to fluctuating energy demands, ensuring efficient grid control and optimized service chaining across distributed nodes.

Ezra Joel Sanders, Liu Xia Fen, Neeraj Raghav Bansal, Madeline Reese Sutton, Thiago Manuel Rocha

This paper presents a heuristic clustering approach for green-aware VM placement in cloud infrastructure. It reduces energy consumption by clustering workloads based on thermal metrics and dynamic resource availability across heterogeneous datacenter nodes.

Griffin Eli Townsend, Zhong Rui Fei, Yash Ratan Jha, Amelia Skye Whitman, Mauricio Dante Paredes

This research introduces a secure data federation architecture using attribute-based encryption (ABE) and blockchain verification. It enables seamless and compliant data sharing across cloud providers while enforcing fine-grained access control and immutable audit trails.

Hunter Kai Dawson, Li Fei Rong, Devansh Ramesh Kulkarni, Violet Claire Morrison, Sergio Alejandro Morales

This paper introduces an explainable AI model for detecting SLA violations in multi-cloud microservices. It provides transparency by attributing causes of violations to specific service bottlenecks, facilitating timely corrective actions and enhancing service reliability.

Beckett Milo Harrison, Wang Li Cheng, Ritu Arvind Bhardwaj, Elise Rowan Price, Mateo Julian Navarro