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In large-scale communication networks consisting of many end hosts and routers, accurate acquisition, measurement, and estimation of communication delays between node pairs are essential for providing high-quality communication services. In several QoS-related performance metrics such as metrics for efficiency, metrics for availability, metrics for reliability, communication delay is one of the key metrics to realize several traffic control mechanisms. Conventional instrumentation and measurement techniques are suitable when the size of the network to be measured is relatively small, or when the number of node pairs to be measured is relatively small. However, in evolving and complex networks, it is not trivial to acquire, measure, and estimate the communication quality at a huge number of routers and end hosts. In this paper, as an initial step toward the realization of estimating communication quality (especially communication delays between node pairs) in a large-scale network, we investigate the potential of graph neural networks with semi- supervised learning for estimating communication delays between node pairs. Our findings include that the average relative error of estimated communication delays is around 10-35% depending on the fraction of the number of measurement nodes, and that communication delays for large-scale networks can be estimated with a high accuracy even if the fraction of the number of measurement nodes is not so large.

Robustness against failure and attack is one of the essential properties of large-scale dynamical system such as power grids, transportation system, communication systems, and computer networks. Despite its popularity and intuitiveness, a major drawback of descriptive robustness metrics such as the size of the largest connected component and the diameter is its computational complexity. On the contrary, predictive metrics such as the spectral radius, the natural connectivity, and the algebraic connectivity are much easier to obtain than descriptive metrics, but the predictability of those measures against different levels and types of failures/attacks has not been well understood. In this paper, we therefore investigate how effectively predictive metrics (spectral measures) can estimate the robustness of a network against random node removal. Our finding includes that, among five types of spectral measures, the effective resistance is most suitable for predicting the largest cluster component size under low node removal ratio, and that the predictability of the effective resistance is stable for various networks generated with different network generation models.

Recently, Content-Centric Networking (CCN) has been extensively studied by networking researchers as a promising network architecture to realize an information-centric network. In CCN, to reduce the amount of redundant Interest and Data packet transmissions, routers can aggregate multiple Interest packets requesting the identical content into a single packet. In the literature, the effect of Interest packet aggregation at CCN router on the performance has been investigated. In this paper, we analyze the impact of delayed packet forwarding at routers on the performance of CCN. With delayed packet forwarding, every router does not immediately forward the Interest packet; instead, it intentionally delays packet forwarding by a fixed amount of time. We analytically derive the content delivery delay with and without delayed packet forwarding at routers to reveal the impact of delayed packet forwarding.

Recently, Content-Centric Networking (CCN) has been extensively studied by networking researchers as a promising network architecture to realize an information-centric network. When a CCN router receives multiple Interest packets requesting identical content, it can aggregate those packets into a single Interest packet in order to reduce the amount of redundant Interest and Data packet transmission. In the literature, it is known that Interest packet aggregation significantly affects the performance of a CCN network. In this paper, we propose a method called Probabilistic Interest Packet Aggregation (PIPA). We investigate the fundamental characteristics of PIPA — in particular, the relation between its Interest packet aggregation probability and the average chunk delivery delay — through both simulation experiments and mathematical analyses. Our findings include that by appropriately aggregating Interest packets at a CCN router using PIPA, the average chunk delivery delay can be reduced by approximately 15% compared with the case without PIPA.