: 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 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. TpRam-Kelly.7z
: A preprint or abstract of the work is hosted on ResearchGate . : It employs Deep Deterministic Policy Gradient (DDPG)
: It uses a Transformer-based attention mechanism to build a performance prediction model for microservice nodes on a system's "critical path". : It uses a Transformer-based attention mechanism to
: 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
The paper addresses the difficulty of optimizing resource allocation in cloud-native environments where microservices have complex dependencies.