Because of the elastic service capability of cloud computing platform, more and more applications are moved here, which makes efficient load balancing into a bottleneck. Considering the unique features of long-connectivity applications which are increasingly popular nowadays, an improved algorithm is proposed based on the weighted least connection algorithm. In the new algorithm, load and processing power are quantified, and single exponential smoothing forecasting mechanism is added. Finally, the article proves by experiments that the new algorithm can reduce the server load tilt, and improve client service quality effectively.

In recent years, the generative adversarial network (GAN) has achieved outstanding performance in the image field and the derivatives of GAN, namely auxiliary classifier GAN (ACGAN) and Wasserstein GAN with gradient penalty (WGAN-GP) have also been widely used, but the GAN applications of nonimage domain are not wide. At the time when the telecommunication fraud is rampant, the signaling data of telephone contain a lot of useful information, which is helpful for distinguishing fraudulent and nonfraudulent calls. In this article, aiming at the problem of limited amount of data and information leakage in the research of telephone signaling data, we adopt WGAN-GP and ACGAN to generate analog data, which confirms distribution of true data. In order to solve the problem of category accuracy of analog data and to enhance the stability and speed of training, we proposed a new network structure for discriminator of GAN based on WGAN-GP and ACGAN. The experiments on telecommunication fraud dataset found that our method obtains better performance on signaling data than base model.

With the advancement of technology in data science and network technology, the world has stepped into the Era of Big Data, and the medical field is rich in data suitable for analysis. Thus, in recent years, there has been much research in medical big data, mainly targeting data collection, data analysis, and visualization. However, very few works provide a full survey of the medical big data on chronic diseases and health monitoring. This review investigates recent research efforts and conducts a comprehensive overview of the work on medical big data, especially as related to chronic diseases and health monitoring. It focuses on the full cycles of the big data processing, which includes medical big data preprocessing, big data tools and algorithms, big data visualization, and security issues in big data. It also attempts to combine common big data technologies with special medical needs by analyzing in detail existing works of medical big data. To the best of our knowledge, this is the first survey that targets chronic diseases and health monitoring big data technologies.