Evaluation of a Realistic Example of Information-Centric Network Metadata Management

This paper presents the performance evaluation of a metadata database (DB) management method that uses realistic numeric examples for IoT Live Data. The method is proposed to reduce the handling costs of Live Data. Live Data are here defined as data that are typically continuously generated by IoT devices and have short lifetimes (e.g., 10 fps surveillance camera images). We have already proposed an evaluation model in which the high locality is significantly featured in Live Data usage. The previous evaluation results are obtained only from general parameter values in statistical distributions. To evaluate realistic situations, this paper assumes that the metadata of Live Data with high usefulness for sharing by many users/services would dominate all metadata. In particular, for such data, we use both surveillance camera images and social networking service contents. The median values and the expected values are set considering the surveillance camera's locality (defined as the average distance between a surveillance camera and the users of its camera images). As a result, the proposed method can reduce the DB update costs by 99.0% while the additional search costs are reduced by up to 27.8% compared with the conventional metadata management method. The additional search costs are negligible compared with the reduction in DB update costs, since the number of searches is much smaller than the number of DB updates with respect to the number of update/search epochs.