A congestion detection on mobile networks becomes the main challenge of cellular carriers and mobile network providers because the mobile network quality easily degrades when many users concentrate on a limited place. Especially when a large-scale event is held, a heavy network congestion interferes with the communication of the participants and local residents. Therefore, the congestion detection process has been performed by several network providers, but has been executed on a high-performance computing resource in a centralized manner, which markedly increases the computing cost. On the other hand, with the wide spread of a large-scale distributed computing environment (e.g., cloud computing), a Complex Event Processing (CEP) system has recently been made available for several purposes. The CEP is a distributed computing system which can identify meaningful events by analyzing a large amount of data stream (e.g., sensor data) in real time. Here, the congestion detection can be considered as a suitable application for the CEP system, where a large amount of traffic logs (i.e., data streams) should rapidly be analyzed in order to detect network congestions (i.e., meaningful events). Therefore, in this study, we propose a new system structure of the CEP-based congestion detection system using distributed computing resources. In the proposed system, processing components are deployed on multiple resources, and execute independent tasks that are carefully extracted from a system procedure of the congestion detection. Through experimental evaluation using computing resources on a popular cloud service (Amazon EC2), it is disclosed that the CEP-based system contributes to achieve the real time detection of congestions on the mobile networks.
2015 by the Information Processing Society of Japan