Modeling of Dynamic Latency Variations Using Auto-Regressive Model and Markov Regime Switching for Mobile Network Access on Trains

User’s experience of network services using large-scale distributed systems is markedly affected by a network condition (i.e., network latency) between a user terminal and a server. In a mobile environment, the network latency fluctuates because a mobile node on the cellular network frequently changes its access network than before when handover or offloading occurs due to users movement on a real world. Many researchers attempt to perform simulation studies on large-scale distributed services provided through mobile networks for revealing the impact of the network condition on the service performance, hence an evaluation model that simulates a realistic state change of latency variation is attracting attention. However, existing studies have assumed only a condition where the tendency of latency variation never changes. Therefore, we propose a new modeling method using a Markov Regime Switching which builds a realistic evaluation model which can represent the dynamic change of the mobile network state. Furthermore, the effectiveness of the proposed modeling method is evaluated based on the actual latency dataset which is collected while a user of a cellular phone moves around within a wide area. Here, with a wide spread of smart phones and tablets in recent years, the Internet connection has become able to be utilized through a cellular and a WiFi network while the mobile user is moving by various kinds of transportation (e.g., train, car). In this study, as a typical example of the transportation, we focus on the Yamanote Line which is the most famous railway loop line used by a large number of office workers in Japan, hence the target dataset which is measured when the mobile user gets on the Yamanote Line is analyzed by the modeling method for building the evaluation model. The evaluation results help us to disclose whether or not the evaluation model constructed by the proposed modeling method can accurately estimate the dynamic variation of the mobile network quality.

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