The paper addresses the difficulty of optimizing resource allocation in cloud-native environments where microservices have complex dependencies.
: It employs Deep Deterministic Policy Gradient (DDPG) , a reinforcement learning technique, to dynamically adjust CPU, memory, and I/O disk allocation based on real-time requirements. TpRam-Kelly.7z
You can find the full text or official citation through these platforms: The paper addresses the difficulty of optimizing resource
: Experimental results using the DeathStarBench benchmark showed that TPRAM can save at least 40.58% of CPU and 15.84% of memory resources while maintaining end-to-end Quality of Service (QoS). Accessing the Paper Accessing the Paper : The official journal publication
: The official journal publication is available at Springer Link .
: A preprint or abstract of the work is hosted on ResearchGate .
The file refers to the research paper titled " Transformer-based performance prediction and proactive resource allocation for cloud-native microservices ," published in Cluster Computing in August 2025.