Author

Bablu Kumar

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Preprint Sep 2026

Stability-Aware Proactive Autoscaling Using a Double Deep Q-Network in Cloud Computing Environments

A double deep Q-Network-based proactive autoscaling approach (DDQN-Proactive) along with Resource Removal Strategy (RRS) that enhances decision-making by decoupling action selection from value evaluation, enabling more stable and adaptive scaling.

Bablu Kumar, Anshul Verma, Rajkummar Buyya · 0 citations

#federated learning Open access Sep 2026

Optimizing resource allocation for federated LLM training via workload forecasting and autoscaling in edge environments

The growing deployment of applications in real-world edge environments is driving an increasing demand for large language model (LLM) training in distributed systems. This demand creates significant challenges for resource management, particularly in resource-constrained edge cloud environments. Federated learning (F...

Bablu Kumar, Anshul Verma, Rajkummar Buyya · Wireless networks · 0 citations