Edge AI Needs Constraint-Aware Storage
Edge AI workloads demand low-latency, privacy-compliant storage. CATER delivers, using policy-driven placement to optimize energy,…
ByPhilip
LSTM + LOF: The Perfect Combo for 6G AI/ML
Combining LSTM for QoS prediction and LOF for anomaly detection, our predictive retraining adapts to…
Predictive Retraining: The Key to SLA-Compliant 6G
Static retraining fails in dynamic 6G networks. Our predictive approach uses LOF and LSTM to…
Apache Ozone Meets CATER: Smarter Edge Storage
Apache Ozone excels at scalable storage—but lacks constraint-aware placement. CATER fills the gap, integrating seamlessly…