Abstract: Disclosed is a profiling-based distributed deep learning job ordering method and apparatus. The ordering method refers to a distributed deep learning job ordering method performed by a computing device including at least a processor and includes profiling each of a plurality of distributed deep learning jobs; and selecting distributed deep learning jobs to concurrently run based on profiling results.

Abstract: Disclosed is a network hypervisor apparatus for providing a software defined networking (SDN)-based virtual network, the network hypervisor apparatus including a data collector configured to collect control traffic data and network topology information for each virtual switch; a control traffic predictor configured to predict future control traffic based on the control traffic data and the network topology information; and a translator configured to translate a control message corresponding to a virtual switch based on a prediction result.

Abstract: Disclosed is a prediction model generation method for predicting training time and resource consumption required for distributed deep learning training and a prediction method using the prediction model. The prediction model generation method is performed by a computing device including at least one processor and includes constructing a training dataset; and generating a prediction model by training a graph neural network (GNN). The training dataset includes input data and result data, the construction of the training dataset includes converting a distributed deep learning training code (distributed training (DT) code) to a graph; and extracting an adjacency matrix and a feature matrix from the graph.