Narrow Results

The development of 5G technology has enabled the cloud-internet of things (IoT) to impact all areas of our lives. Sensors in cloud-IoT generate large-scale data, and the demand for massive data processing is also increasing. The performance of a single machine can no longer meet the needs of existing users. In contrast, a data center (DC) integrates computing power and storage resources through a specific network topology and satisfies the need to process massive data. Regarding large-scale heterogeneous traffic in DCs, differentiated traffic scheduling on demand reduces transmission latency and improves throughput. Therefore, this paper presents a traffic scheduling method based on deep Q-networks (DQN). This method collects network parameters, delivers them to the environment module, and completes the environment construction of network information and reinforcement learning elements through the environment module. Thus, the final transmission path of the elephant flow is converted based on the action given by DQN. The experimental results show that the method proposed in this paper effectively reduces the transmission latency and improves the link utilization and throughput to a certain extent.

Exercise has long been known to improve cardiovascular health, energy metabolism, and well-being. However, myocardial cell responses to exercise are complex and multifaceted due to their molecular pathways. To understand cardiac physiology and path physiology, one must understand these pathways, including energy autophagy. In recent years, deep learning techniques, IoT devices, and cloud computing infrastructure have enabled real-time, large-scale biological data analysis. The objective of this work is to extract and analyze autophagy properties in exercise-induced cardiac cells in a cloud-IoT context using deep learning, more especially an autoencoder. The Shanghai University of Sport Ethics Committee for Science Research gave its approval for the data collection, which involved 150 male Sprague–Dawley (SD) rats that were eight weeks old and in good health. The $Z$-score normalization method was used to standardize the data. Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to analyze deep learning with Autoencoder, the autography energy of exercise myocardial cells within a cloud-IoT. To capture the intricate myocardial energy autophagy during exercise, we introduced the DMO-GCNN-Autoencoder, a Dwarf Mongoose Optimized Graph Convolutional Neural Network. The results showed that the proposed network’s performance matches that of the existing methods.