The paper addresses the difficulty of optimizing resource allocation in cloud-native environments where microservices have complex dependencies.
: A preprint or abstract of the work is hosted on ResearchGate . TpRam-Kelly.7z
: 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. The paper addresses the difficulty of optimizing resource
: The official journal publication is available at Springer Link . : The official journal publication is available at
You can find the full text or official citation through these platforms:
: It uses a Transformer-based attention mechanism to build a performance prediction model for microservice nodes on a system's "critical path".
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.