This chapter describes the application scenarios, architecture, and key components of HCloud. It provides the details of the data analysis services in HCloud. The chapter explains the details of the Map-Reduce paradigm immersed in the platform, as well as the health care services that HCloud can provide. It also provides information on performance testing and evaluation. One challenging task for the health care cloud system is to handle the multi-modal and nonstationary characteristics of special physiological signals, such as those for high blood pressure, electrocardiography, and photoplethysmography. It is quite an inefficient job for a cloud system to store the numeric small-size physiological signal data on the ordinary distributed file system. Seamless data fusion from signals collected for data processing in the cloud should be a concern. Actually, the main tasks of HCloud are physiological data processing and computing, which can affect the performance of the whole system.