You can find the full text or official citation through these platforms:
: The official journal publication is available at Springer Link .
The paper addresses the difficulty of optimizing resource allocation in cloud-native environments where microservices have complex dependencies.
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.
The file name is a shorthand for the framework (Transformer-based Prediction and Resource Adaption Method) and likely one of its primary authors or a related contributor, such as Yang Chen or Hongyan Xia (whose research is often associated with these models). Paper Summary: TPRAM
: 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.
: It uses a Transformer-based attention mechanism to build a performance prediction model for microservice nodes on a system's "critical path".
: A preprint or abstract of the work is hosted on ResearchGate .
You can find the full text or official citation through these platforms:
: The official journal publication is available at Springer Link .
The paper addresses the difficulty of optimizing resource allocation in cloud-native environments where microservices have complex dependencies.
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.
The file name is a shorthand for the framework (Transformer-based Prediction and Resource Adaption Method) and likely one of its primary authors or a related contributor, such as Yang Chen or Hongyan Xia (whose research is often associated with these models). Paper Summary: TPRAM
: 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.
: It uses a Transformer-based attention mechanism to build a performance prediction model for microservice nodes on a system's "critical path".
: A preprint or abstract of the work is hosted on ResearchGate .
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