Research on Design and Application of National Fitness System

In order to improve the physical quality of the national people, a national fitness system is designed and applied to practice. Design the overall architecture of the national fitness system, including the perception layer, network layer, and application layer. The perception layer mainly uses Internet of Things gateway, central machine, wireless perception node, and fitness data dashboard to obtain fitness data. The network layer mainly uses WiFi, 4G, Ethernet, and other public networks to transmit fitness data, fitness guidance data, and equipment operation and maintenance data. The application layer provides data storage, device management, user management, and client services. On this basis, through the collection of users’ fitness data rating data, the data are transformed into fitness data rating matrix, and the matrix is analyzed and calculated to realize the intelligent recommendation of fitness data and complete the design of national fitness data recommendation algorithm. The test results show that the system can meet the requirements of normal use, good compatibility, and user score is high and has high practical application value.

Fog-Based CDN Framework for Minimizing Latency of Web Services Using Fog-Based HTTP Browser

Cloud computing has been a dominant computing paradigm for many years. It provides applications with computing, storage, and networking capabilities. Furthermore, it enhances the scalability and quality of service (QoS) of applications and offers the better utilization of resources. Recently, these advantages of cloud computing have deteriorated in quality. Cloud services have been affected in terms of latency and QoS due to the high streams of data produced by many Internet of Things (IoT) devices, smart machines, and other computing devices joining the network, which in turn affects network capabilities. Content delivery networks (CDNs) previously provided a partial solution for content retrieval, availability, and resource download time. CDNs rely on the geographic distribution of cloud servers to provide better content reachability. CDNs are perceived as a network layer near cloud data centers. Recently, CDNs began to perceive the same degradations of QoS due to the same factors. Fog computing fills the gap between cloud services and consumers by bringing cloud capabilities close to end devices. Fog computing is perceived as another network layer near end devices. The adoption of the CDN model in fog computing is a promising approach to providing better QoS and latency for cloud services. Therefore, a fog-based CDN framework capable of reducing the load time of web services was proposed in this paper. To evaluate our proposed framework and provide a complete set of tools for its use, a fog-based browser was developed. We showed that our proposed fog-based CDN framework improved the load time of web pages compared to the results attained through the use of the traditional CDN. Different experiments were conducted with a simple network topology against six websites with different content sizes along with a different number of fog nodes at different network distances. The results of these experiments show that with a fog-based CDN framework offloading autonomy, latency can be reduced by 85% and enhance the user experience of websites.

Layer-4 Load Balancer for Flow Size Prediction with TCP/UDP Separation Using P4

Nowadays, datacenters become more complicated and handle many more users’ requests. Custom protocols are becoming more demanded, and an advanced load balancer to distribute the requests among servers is essential to serve the users quickly and efficiently. P4 introduced a new way to manipulate all packet headers. Therefore, by making use of the P4 ability to decapsulate the transport layer header, a new algorithm of load balancing is proposed. The algorithm has three main parts. First, a TCP/UDP separation is used to separate the flows based on the network layer information about the used protocol in the transport layer. Second, a flow size prediction technique is adopted, which relies on the service port number of the transport layer. Lastly, a probing system is considered to detect and solve the failure of the link and server. The proposed load balancer enhances response time of both resources usage and packet processing of the datacenter. Also, our load balancer improves link failure detection by developing a custom probing protocol.

Research on Network Layer Recursive Reduction Model Compression for Image Recognition

ResNet has been widely used in the field of machine learning since it was proposed. This network model is successful in extracting features from input data by superimposing multiple layers of neural networks and thus achieves high accuracy in many applications. However, the superposition of multilayer neural networks increases their computational cost. For this reason, we propose a network model compression technique that removes multiple neural network layers from ResNet without decreasing the accuracy rate. The key idea is to provide a priority term to identify the importance of each neural network layer, and then select the unimportant layers to be removed during the training process based on the priority of the neural network layers. In addition, this paper also retrains the network model to avoid the accuracy degradation caused by the deletion of network layers. Experiments demonstrate that the network size can be reduced by 24.00%–42.86% of the number of layers without reducing the classification accuracy when classification is performed on CIFAR-10/100 and ImageNet.