Tarp-data

This dataset contains the source data and experimental result data required by the tarp project. The cover depicts our proposed adaptive resource allocation approach based on graph neural networks for optimizing qos-aware interactive microservices in cloud computing. This method uses DAG topology to extract the global characteristics of microservices, and adaptively generates microservice resource allocation strategies, which can effectively use microservice resources while ensuring the quality of service. This method uses EGAT to extract microservice features and uses reinforcement learning to generate resource allocation policies.First, we define the microservice state graph.Then, we use EGAT to generate embeddings for each node in the graph by extracting the hidden features of resources and network metrics.Based on the message pass paradigm of graph neural networks (GNN), we design the microservice feature passing to capture correlations between microservices, thereby improving the transferability of our approach. Finally, we use DDPG to model microserviceas in a uniform and self-adaptive manner.