Nonparametric self-exciting models for computer network traffic

Point Process Models for Self-Similar Network Traffic, with Applications

Self-similar processes based on fractal point processes (FPPs) provide natural and attractive network traffic models. We show that the point process formulation yields a wide range of FPPs which in turn yield a diversity of parsimonious, computationally efficient, and highly practical asymptotic second-order self-similar processes. Using this framework, we show that the relevant second-order fr...

Nonparametric Network Models for Link Prediction

Many data sets can be represented as a sequence of interactions between entities—for example communications between individuals in a social network, protein-protein interactions or DNA-protein interactions in a biological context, or vehicles’ journeys between cities. In these contexts, there is often interest in making predictions about future interactions, such as who will message whom. A pop...

Nonparametric Functional Calibration of Computer Models

Standard methods in computer model calibration treat the calibration parameters as constant throughout the domain of control inputs. In many applications, systematic variation may cause the best values for the calibration parameters to change across different settings. When not accounted for in the code, this variation can make the computer model inadequate. We propose a framework for modeling ...

Self-similar Traffic Models

Modeling Self-Similar Traffic for Network Simulation

In order to closely simulate the real network scenario thereby verify the effectiveness of protocol designs, it is necessary to model the traffic flows carried over realistic networks. Extensive studies [1] showed that the actual traffic in access and local area networks (e.g., those generated by ftp and video streams) exhibits the property of self-similarity and long-range dependency (LRD) [2]...