A high-performance and scalable distributed storage and computing system for IMS services

Because of the rapid growth of the number of mobile user requests and media files on demand, system performance could be negatively impacted and the management of different sorts of media files becomes costly and increasingly difficult. In this article we propose an innovative architecture combining the IP Multimedia Subsystem (IMS) platform and the Hadoop system for use in the distributed storage of IMS service resources and for the purposes of the computing service that is proposed in this work for responding to mobile end-users' needs in terms of mobile computing through the IMS network. As a result, we obtain a controllable Hadoop-based data center for telecommunications service providers. Moreover, for the proposed computing service, MapReduce analysis is also used to create new revenues and improve the IMS computing capabilities. In this article, we present a high-performance and scalable distributed storage and computing system for IMS services through different scenarios of service provisioning, storing and computing processes. Via experiments, the system performance is determined. Furthermore, the experimental results prove the system availability and scalability by sharing out more distributed resources for further IMS services using Hadoop Distributed File System (HDFS), YARN, and the Hadoop distributed cache mechanism. The proposed architecture in addition considerably minimizes response time, maximizes throughput and server utilization gets improved.