Stochastic methods for measurement-based network control

The main task of network administrators is to ensure that their network functions properly. Whether they manage a telecommunication or a road network, they generally base their decisions on the analysis of measurement data. Inspired by such network control applications, this dissertation investigates several stochastic modelling techniques for data analysis. The focus is on two areas within the field of stochastic processes: change point detection and queueing theory. Part I deals with statistical methods for the automatic detection of change points, being changes in the probability distribution underlying a data sequence. This part starts with a review of existing change point detection methods for data sequences consisting of independent observations. The main contribution of this part is the generalisation of the classic cusum method to account for dependence within data sequences. We analyse the false alarm probability of the resulting methods using a large deviations approach. The part also discusses numerical tests of the new methods and a cyber attack detection application, in which we investigate how to detect dns tunnels. The main contribution of Part II is the application of queueing models (probabilistic models for waiting lines) to situations in which the system to be controlled can only be observed partially. We consider two types of partial information. Firstly, we develop a procedure to get insight into the performance of queueing systems between consecutive system-state measurements and apply it in a numerical study, which was motivated by capacity management in cable access networks. Secondly, inspired by dynamic road control applications, we study routing policies in a queueing system for which just part of the jobs are observable and controllable

A comprehensive approach to MPSoC security: achieving network-on-chip security : a hierarchical, multi-agent approach

Multiprocessor Systems-on-Chip (MPSoCs) are pervading our lives, acquiring ever increasing relevance in a large number of applications, including even safety-critical ones. MPSoCs, are becoming increasingly complex and heterogeneous; the Networks on Chip (NoC paradigm has been introduced to support scalable on-chip communication, and (in some cases) even with reconfigurability support. The increased complexity as well as the networking approach in turn make security aspects more critical. In this work we propose and implement a hierarchical multi-agent approach providing solutions to secure NoC based MPSoCs at different levels of design. We develop a flexible, scalable and modular structure that integrates protection of different elements in the MPSoC (e.g. memory, processors) from different attack scenarios. Rather than focusing on protection strategies specifically devised for an individual attack or a particular core, this work aims at providing a comprehensive, system-level protection strategy: this constitutes its main methodological contribution. We prove feasibility of the concepts via prototype realization in FPGA technology

Change-point Problem and Regression: An Annotated Bibliography

The problems of identifying changes at unknown times and of estimating the location of changes in stochastic processes are referred to as the change-point problem or, in the Eastern literature, as disorder . The change-point problem, first introduced in the quality control context, has since developed into a fundamental problem in the areas of statistical control theory, stationarity of a stochastic process, estimation of the current position of a time series, testing and estimation of change in the patterns of a regression model, and most recently in the comparison and matching of DNA sequences in microarray data analysis. Numerous methodological approaches have been implemented in examining change-point models. Maximum-likelihood estimation, Bayesian estimation, isotonic regression, piecewise regression, quasi-likelihood and non-parametric regression are among the methods which have been applied to resolving challenges in change-point problems. Grid-searching approaches have also been used to examine the change-point problem. Statistical analysis of change-point problems depends on the method of data collection. If the data collection is ongoing until some random time, then the appropriate statistical procedure is called sequential. If, however, a large finite set of data is collected with the purpose of determining if at least one change-point occurred, then this may be referred to as non-sequential. Not surprisingly, both the former and the latter have a rich literature with much of the earlier work focusing on sequential methods inspired by applications in quality control for industrial processes. In the regression literature, the change-point model is also referred to as two- or multiple-phase regression, switching regression, segmented regression, two-stage least squares (Shaban, 1980), or broken-line regression. The area of the change-point problem has been the subject of intensive research in the past half-century. The subject has evolved considerably and found applications in many different areas. It seems rather impossible to summarize all of the research carried out over the past 50 years on the change-point problem. We have therefore confined ourselves to those articles on change-point problems which pertain to regression. The important branch of sequential procedures in change-point problems has been left out entirely. We refer the readers to the seminal review papers by Lai (1995, 2001). The so called structural change models, which occupy a considerable portion of the research in the area of change-point, particularly among econometricians, have not been fully considered. We refer the reader to Perron (2005) for an updated review in this area. Articles on change-point in time series are considered only if the methodologies presented in the paper pertain to regression analysis

Extensions of Markov Modulated Poisson Processes and Their Applications to Deep Earthquakes

The focus of this thesis is on the Markov modulated Poisson process (MMPP) and its extensions, aiming to propose appropriate statistical models for the occurrence patterns of main New Zealand deep earthquakes. Such an attempt might be beyond the scope of the MMPP and its extensions, however we hope its main patterns can be characterized by current models proposed in three parts of the thesis. The first part of the thesis is concerned with introductions and preliminaries of discrete time hidden Markov models (HMMs) and MMPP. The exibility in model formulation and openness in model framework of HMMs are reviewed in this part, suggesting also possible extensions of MMPP. The second part of the thesis is mainly about several extensions of MMPP. One extension of MMPP is by associating each occurrence of MMPP with a mark. Such an extension is potentially useful for spatial-temporal modelling or other point processes with marks. A special case of this type of extension is by allowing the multiple observations of MMPP synchronized together under the same Markov chain. This extension opens the possibility of modelling multiple point process observations with weak dependence. The third extension is motivated by the attempt to describe small scale temporal clustering existing in the deep earthquakes via treating the recognized aftershocks as marks which itself forms a finite point process. The rest of the second part focuses on some information theoretical aspects of MMPPs such as the entropy rate of the underlying Markov chain and observed point process respectively and their mutual information rate. A conjecture on the possible links between mutual information rate of MMPP and the Fisher information of the estimated parameters is suggested. The second part on extensions of MMPP is featured by the derivation of the likelihood and complete likelihood, parameter estimation via EM algorithm, state smoothing estimation and model evaluation through systematic applications of rescaling theory of multivariate point processes and marked point processes. The third part of the thesis includes the applications of these methods to the deep earthquakes in New Zealand. We first evaluate the data coverage, catalogue completeness and explore its descriptive characteristics and empirical properties such as epicentral distributions, depth distributions and magnitude distributions. Clustering behavior is studied via the second order moment analysis of point processes in the chapter 8. We also apply, the stress release models and the ETAS models which are usually used for shallow earthquakes, to the New Zealand deep earthquakes and provide tentative explanations of why they are not satisfactory for the deep earth-quakes. The chapter 9 is on the applications of MMPP and its extensions to the New Zealand deep earthquakes. Conclusions and future studies are presented in chapter 10

API design and implementation of a management interface for SDN whitebox switches

For the past few years, cloud computing has emerged to be one of the most rapidly growing plaforms. This growth must be supported from the data centers, that look to provide the best possible service, while minimising energy and infrastructure costs. As such, many service providers are moving to Software Defined Networking (SDN) based platforms, that allow for new concepts such as the separation of the control and data planes, and the adoption of open source material, in both the switches, in the form of whitebox switches, and the network controllers. BISDN is a company that is developing a SDN controller, that allows to use the linux networking tools, like netlink, to configure and manage ports on switches. The proposed problem, is then extending the existing platform to be able to report statistics such as flow-counts on the switches, the number of packets received, dropped, transmitted in the ports, so that the data center operators can have the best possible information on the state of their network, and act in case of failures and malfunctions

Simulation optimisation: An expert mechanism approach

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

MODEL-BASED APPROACH TO THE UTILIZATION OF HETEROGENEOUS NON-OVERLAPPING DATA IN THE OPTIMIZATION OF COMPLEX AIRPORT SYSTEMS

Simulation and optimization have been widely used in air transportation, particularly when it comes to determining how flight operations might evolve. However, with regards to passengers and the services provided to them, this is not the case in large part because the data required for such analysis is harder to collect, requiring the timely use of surveys and significant human labor. The ubiquity of always--connected smart devices and the rise of inexpensive smart devices has made it possible to continuously collect passenger information for passenger-centric solutions such as the automatic mitigation of passenger traffic. Using these devices, it is possible to capture dwell times, transit times, and delays directly from the customers. The data; however, is often sparse and heterogeneous, both spatially and temporally. For instance, the observations come at different times and have different levels of accuracy depending on the location, making it challenging to develop a precise network model of airport operations. The objective of this research is to provide online methods to sequentially correct the estimates of the dynamics of a system of queues despite noisy, quickly changing, and incomplete information. First, a sequential change point detection scheme based on a generalized likelihood ratio test is developed to detect a change in the dynamics of a single queue by using a combination of waiting times, time spent in queue, and queue-length measurements. A trade-off is made between the accuracy of the tests, the speed of the tests, the costs of the tests, and the value of utilizing the observations jointly or separately. The contribution is a robust detection methodology that quickly detects a change in queue dynamics from correlated measurements. In the second part of the work, a model-based estimation tool is developed to update the service rate distribution for a single queue from sparse and noisy airport operations data. Model Reference Adaptive Sampling is used in-the-loop to update a generalized gamma distribution for the service rates within a simulation of the queue at an airport’s immigration center. The contribution is a model predictive tool to optimize the service rates based on waiting time information. The two frameworks allow for the analysis of heterogeneous passenger data sources to enable the tactical mitigation of airport passenger traffic delays.Ph.D