With the advance of the recent communications network environment into the All-IP data service, the network structure is becoming more complicated and expectations for network operation efficiency and service competitiveness are growing higher than ever. In addition, corporate expenditure and budget losses for network malfunction continues to grow exponentially. Network operators endeavor to detect problems immediately in order to prevent network error or poor service quality, and to figure out the root cause of problems in a prompt manner. They do this by utilizing various types of data produced in the network in a bid to maintain entire network stability and improve service quality. In particular, they conduct real-time monitoring of end-to-end quality and check on effective uses of network resources to respond promptly and appropriately to customer complaints concerning service quality. Furthermore, they concentrate all their energies on strengthening service competitiveness by detecting potential network problems ahead of time and promoting “preemptive and proactive service management.” However, they still face challenges in managing networks due to the many problems of the real network operation environment.
Each network equipment produces periodic data from its own standpoint. When the network is operated based on the data provided by the given equipment, there can be a large gap between the given data and the service quality that customers experience, and it takes a long time to figure out the root cause of problems, which in turn leads to complaints from customers. Moreover, as the service quality that each customer experiences varies, there is a growing need for managing the service quality individually to improve service competitiveness.
The network operation systems process a wide range of data, such as real-time fault data and statistics data provided by each piece of equipment, and various log data, which show the quality of service provided to individual customers. In particular these days, the systems collect and process packets, which flow within the network to figure out the quality of the All-IP network service that individual customers experience. These systems should be operated based on a Big Data processing platform verified on a high-volume network as they need to perform a real-time processing of billions of records of network data each day depending on the network scale and the number of customers.
Network data, particularly traffic data is used to operate the network effectively. For instance, it is utilized as a basic information to provide a stable service through controlling heavy traffic users and applications. Moreover, it can be used for the estimation of the capacity of each equipment in the network and also for the establishment of marketing strategies based on the traffic patterns of customers. However, most of the operations support systems were solely designed to facilitate the network operation and thus, either a new system needs to be introduced or existing systems should be reformed in line with the needs of other departments, such as network engineering, customer care center or marketing. As such, the systems used by network operators should provide basic data that is multi-purpose and fit for use by many departments.
Mobigen offers solutions to support various operational tasks required for the improvement of communications service quality. We also contribute to securing service quality competitiveness and improving operational efficiency by putting ourselves in our customers??shoes. Our operations support systems process, store, and analyze various types of information collected from a wide range of wired and wireless communications networks and pieces of service equipment. Mobigen solution provide various functionality to detect errors and analyze problems occurred at each layer including individual subscribers, devices, networks, and services. In particular, we provide an Advanced Analytics environment based on our Big Data platform in which it is possible to process a vast amount of network data in real-time and realize Preemptive Monitoring, Anomaly Detection, and Root Cause Analysis.
Backlinks to this page