This work proposes a novel approach to log anomaly detection using temporal attention mechanisms. By capturing time-sensitive dependencies across layers, the model improves fault diagnosis and reduces false positives in distributed multi-cloud environments.

Kendall Joseph Price, Li Fang Chen, Aniket Vishnu Patil, Grace Evelyn Hamilton, Pablo Andres Morales

This paper presents a reinforcement learning-based elasticity manager for stateful microservices. The framework learns optimal resource scaling policies in dynamic multi-tenant environments to maintain service-level objectives and resource fairness.

Julian Maxwell Rhodes, Zhao Yu Xin, Ritesh Devansh Raghavan, Charlotte Mae Jenkins, Tomas Emanuel Herrera

This study introduces an event-context model that forecasts resource needs in serverless functions. By identifying recurring patterns and adapting execution environments preemptively, the system avoids latency penalties and enables seamless scalability.

Corbin Elijah Steele, Xu Ming Tao, Nikhil Jayant Desai, Hannah Claire Dawson, Luciano Mario Ortiz

This research proposes a digital twins framework for resilient manufacturing in the cloud. It models machine behavior under faults, dynamically adjusting workflows in real time, enhancing robustness and reducing unplanned downtime in smart industrial ecosystems.

Wesley Damian Clarke, Liu Wen Qiang, Nishant Raghav Bhatnagar, Sophie Isla Donovan, Hugo Alejandro Martinez

This work presents a zero-trust framework to enforce data and access policies for federated cloud-based AI workflows. It enables secure multi-tenant execution without centralized trust brokers while preserving data confidentiality and minimizing latency overhead.

Travis Leon Grant, Zhang Hui Min, Ranjan Harish Venkat, Madeline Aurora Thompson, Leonardo Jose Paredes

This study introduces a temporal convolutional approach to forecast SLA violations before occurrence. It uses event profiling and anomaly sequences to issue early warnings, enabling cloud service providers to meet contractual guarantees proactively and efficiently.

Xavier Jerome Foster, Lin Yao Cheng, Tanay Deepak Rao, Megan Elise Wallace, Francisco Javier Morales