A Dynamic Load Balancing Model for Concurrently Connected Users in U-Healthcare Monitoring Systems

U-healthcare systems are based on a ubiquitous and wireless computing and communication environment. They are comprised of the U-healthcare management center, electronic medical records (EMR) system, and the associated services for users and patients. The U-healthcare management center performs continuous monitoring and provides support services in multiple areas, requiring careful allocation of the limited service resources to provide customized healthcare services for users of the mobile distributed system. When the number of locally connected users increases rapidly, a mobile allocation and distribution server can be imbalanced by the load on service resources, resulting in delayed services. This study proposes a dynamic load balancing model for reducing the load of users on service resources and supporting efficient response services in a mobile distributed system. The proposed dynamic load balancing model clusters the system resources of servers dynamically, according to each users' movement and time. The dynamic clustering of system resources uses wFCM (weighted Fuzzy C-Means), which changes the cluster center by transforming existing FCM (Fuzzy C-Means) from a fixed weight to a dynamic one. Using wFCM, the load balance can be maintained, based on the usage rate of service resources, such as CPU, memory, and network. In addition, the balance between QoS (Quality of Service) requests and network response times can be maintained by adding an abstraction layer between application services and network infrastructure. Therefore, when the proposed model is applied to a U-healthcare monitoring system, the system can perform near real-time monitoring of service users in the mobile distributed environment, and effectively address emergent situations. This study evaluates the response time of the implemented model in relation to the number of concurrently connected users, and confirms that the proposed model is faster in response and service processing than existing WLC (Weighted Least-Connection Scheduling) and FCM (Fuzzy C-Means).