Next Generation Network (NGN) is envisioned to be an inter-working environment of heterogeneous networks of wired and wireless access networks, PSTN, satellites, broadcasting, etc., all interconnected through the service provider's IP backbone and the Internet. NGN uses multiple broadband, QoS-enabled transport technologies and service-related functions independent from underlying transport-related technologies. The operations and management of such interconnected networks are expected to be much more difficult and important than the traditional network environment. In this paper, we present an overview of the current status towards the management of NGN and discuss challenges in operating and managing NGN. We also present the operations and management requirements of NGN in accordance with the challenges. We then present standardization activities of NGN management and some of the notable research and development efforts related to NGN management.

Histogram sequences represent high-dimensional time-series converted from images by space filling curves (SFCs). To overcome the high-dimensionality nature of histogram sequences (e.g., 106 dimensions for a 1024×1024 image), we often use lower-dimensional transformations, but the tightness of their lower-bounds is highly affected by the types of SFCs. In this paper we attack a challenging problem of evaluating which SFC shows the better performance when we apply the lower-dimensional transformation to histogram sequences. For this, we first present a concept of spatial locality and propose spatial locality preservation metric (SLPM in short). We then evaluate five well-known SFCs from the perspective of SLPM and verify that the evaluation result concurs with the actual transformation performance. Finally, we empirically validate the accuracy of SLPM by providing that the Hilbert-order with the highest SLPM also shows the best performance in k-NN (k-nearest neighbors) search.