: 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
You can find the full text or official citation through these platforms: TpRam-Kelly.7z
: The official journal publication is available at Springer Link . TpRam-Kelly.7z
The paper addresses the difficulty of optimizing resource allocation in cloud-native environments where microservices have complex dependencies. TpRam-Kelly.7z
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