Event-driven Principles and Complex Event Processing for Self-adaptive Network Analysis and Surveillance Systems

Event-driven approaches and Complex Event Processing (CEP) have the potential to aid in tackling the complex requirements and challenges of monitoring contemporary computer networks. The applicability of such methods, however, depends on, e.g., architectural considerations, data processing performance, or usability. In this thesis, we study the applicability of event-driven principles and CEP for analyzing and surveying computer networks and present ways for improving the applicability of these paradigms. The main contributions that are presented and discussed in this thesis are: an analysis of important properties of network analysis and surveillance, the introduction of a corresponding Event-driven Architecture (EDA) for addressing these requirements, the empirical evaluation of the proposed EDA using a prototype implementation, the development of cooperative and self-adaptive methods for addressing performance and usability issues, and the development of techniques for improving the integration of components implemented in different languages in event-driven systems. Assuring and maintaining the proper operation of computer networks is as crucial as assuring the proper operation of the Information Technology (IT) systems they connect. However, collecting and analyzing information about computer networks, which is required for assuring their proper operation, is increasingly challenging because of, e.g., the growing logical and spatial extent of computer networks, accelerated changes in computer network structures and network traffic, or near real-time requirements. Furthermore, a wide variety of methods for network analysis and surveillance exists and for acquiring comprehensive information at optimal resource requirements these various methods have to be combined with a converging approach. Based on the results of an analysis of important properties and requirements for network analysis and surveillance, we propose an approach which leverages event-driven paradigms such as EDA and CEP for addressing the complex mix of requirements in this field and for enabling convergence of the various existing methods. We evaluate our proposed approach with a case study and performance benchmarks using a prototype. Our results show that our approach is a good fit for addressing the complex mix of requirements and that it is feasible from a performance perspective. In contrast to other related recent research, which is limited to specific use cases, we propose a generic and versatile event-driven approach for universal network analysis and surveillance. Moreover, we present techniques for further improving network analysis and surveillance. While our general approach already constitutes an important improvement, we also propose and investigate further innovations. Based on the evaluation of our approach, we consider distributed operation, usability, performance in distributed deployments and of sensors, integration of data sources, and the interoperation of implementations in different programming languages in event-driven systems as most important aspects for further improvement. For improving the operation, usability, and performance in distributed contexts, we develop an approach for cooperative and self-adaptive data acquisition using the example of packet capturing. In order to research ways for advancing the operation of sensors and integration of data sources, we use the example of packet capturing with the Java Virtual Machine (JVM), for which we develop and analyze various improvements at various abstraction levels such as data extraction via a Domain Specific Language (DSL) or self-adaptive adjustments based on performance constraints. Even though packet capturing with the JVM was already employed in other research, these studies only consider the overall systems such that neither the specific implications of JVM-based packet capturing nor methods for improving the performance in this scenario were discussed in detail yet. Furthermore, we analyze the impact of programming language barriers in event-driven systems and present a batch-based approach for increasing the data exchange throughput. In conclusion, we improve the state-of-the-art of network analysis and surveillance. Our work aims on taking the next step towards holistic network analysis and surveillance by addressing distribution, convergence, usability, and performance aspects. We demonstrate the benefits and evaluate the applicability of event-driven data processing paradigms and show how self-adaptivity and cooperation can further improve the capabilities

Discovering social networks from event logs

Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log from a Dutch organization

Dynamic bandwidth allocation using infinitesimal perturbation analysis

Advances in network management and switching technologies make dynamic bandwidth allocation of logical networks built on top of a physical network possible. Previous proposed dynamic bandwidth allocation algorithms are based on simplified network model. The analytical model is valid only under restrictive assumptions. Infinitesimal Perturbation Analysis, a technique which estimates the gradients of the functions in discrete event dynamic systems by passively observing the system, is used to estimate delay sensitivities under general traffic patterns. A new dynamic bandwidth allocation algorithm using on-line sensitivity estimation is proposed. Simulation results show that the approach further improves network performance. Implementation of the proposed algorithm in operational networks is also discussed.published_or_final_versio

Integration of e-business strategy for multi-lifecycle production systems

Internet use has grown exponentially on the last few years becoming a global communication and business resource. Internet-based business, or e-Business will truly affect every sector of the economy in ways that today we can only imagine. The manufacturing sector will be at the forefront of this change. This doctoral dissertation provides a scientific framework and a set of novel decision support tools for evaluating, modeling, and optimizing the overall performance of e-Business integrated multi-lifecycle production systems. The characteristics of this framework include environmental lifecycle study, environmental performance metrics, hyper-network model of integrated e-supply chain networks, fuzzy multi-objective optimization method, discrete-event simulation approach, and scalable enterprise environmental management system design. The dissertation research reveals that integration of e-Business strategy into production systems can alter current industry practices along a pathway towards sustainability, enhancing resource productivity, improving cost efficiencies and reducing lifecycle environmental impacts. The following research challenges and scholarly accomplishments have been addressed in this dissertation: Identification and analysis of environmental impacts of e-Business. A pioneering environmental lifecycle study on the impact of e-Business is conducted, and fuzzy decision theory is further applied to evaluate e-Business scenarios in order to overcome data uncertainty and information gaps; Understanding, evaluation, and development of environmental performance metrics. Major environmental performance metrics are compared and evaluated. A universal target-based performance metric, developed jointly with a team of industry and university researchers, is evaluated, implemented, and utilized in the methodology framework; Generic framework of integrated e-supply chain network. The framework is based on the most recent research on large complex supply chain network model, but extended to integrate demanufacturers, recyclers, and resellers as supply chain partners. Moreover, The e-Business information network is modeled as a overlaid hypernetwork layer for the supply chain; Fuzzy multi-objective optimization theory and discrete-event simulation methods. The solution methods deal with overall system parameter trade-offs, partner selections, and sustainable decision-making; Architecture design for scalable enterprise environmental management system. This novel system is designed and deployed using knowledge-based ontology theory, and XML techniques within an agent-based structure. The implementation model and system prototype are also provided. The new methodology and framework have the potential of being widely used in system analysis, design and implementation of e-Business enabled engineering systems

An analysis of sound event detection under acoustic degradation using multi-resolution systems

The Sound Event Detection task aims to determine the temporal locations of acoustic events in audio clips. In recent years, the relevance of this field is rising due to the introduction of datasets such as Google AudioSet or DESED (Domestic Environment Sound Event Detection) and competitive evaluations like the DCASE Challenge (Detection and Classification of Acoustic Scenes and Events). In this paper, we analyze the performance of Sound Event Detection systems under diverse artificial acoustic conditions such as high-or low-pass filtering and clipping or dynamic range compression, as well as under an scenario of high overlap between events. For this purpose, the audio was obtained from the Evaluation subset of the DESED dataset, whereas the systems were trained in the context of the DCASE Challenge 2020 Task 4. Our systems are based upon the challenge baseline, which consists of a Convolutional-Recurrent Neural Network trained using the Mean Teacher method, and they employ a multiresolution approach which is able to improve the Sound Event Detection performance through the use of several resolutions during the extraction of Mel-spectrogram features. We provide insights on the benefits of this multiresolution approach in different acoustic settings, and compare the performance of the single-resolution systems in the aforementioned scenarios when using different resolutions. Furthermore, we complement the analysis of the performance in the high-overlap scenario by assessing the degree of overlap of each event category in sound event detection datasetsThis research and the APC were supported by project DSForSec (grant number RTI2018- 098091-B-I00) funded by the Ministry of Science, Innovation and Universities of Spain and the European Regional Development Fund (ERDF

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Department of Urban and Environmental Engineering (Disaster Management Engineering)Seismic risk assessment has recently emerged as an important issue for infrastructure systems because of their vulnerability to seismic hazards. Earthquakes can have significant impacts on transportation networks such as bridge collapse and the resulting disconnections in a network. One of the main concerns is the accurate estimation of the seismic risk caused by the physical damage of bridges and the reduced performance of the associated transportation network. This requires estimating the performance of a bridge transportation network at the system level. Moreover, it is necessary to deal with various possible earthquake scenarios and the associated damage states of component bridges considering the uncertainty of earthquake locations and magnitudes. To perform the seismic risk assessment of a bridge transportation network, system reliability is required. It is a challenging task for several reasons. First, the seismic risk itself contains a great deal of uncertainty, which comprises location, magnitude, and the resulting intensity of possible earthquakes in a target network. Second, the system performance of a bridge transportation network after the seismic event needs to be estimated accurately, especially for realistic and complex networks. Third, the seismic risk assessment employing system reliability may increase the computational costs and can be time-consuming tasks, because it requires dealing with various possible earthquake scenarios and the resulting seismic fragility of component bridges. Fourth, a precise performance measure of the system needs to be introduced. In this study, a new method is proposed to assess the system-level seismic risk of bridge transportation networks considering earthquake uncertainty. In addition, a new performance measure is developed to help risk-informed decision-making regarding seismic hazard mitigation and disaster management. For the tasks, first of all, a matrix-based system reliability framework is developed, which performs the estimation of a bridge transportation network subjected to earthquakes. Probabilistic seismic hazard analysis (PSHA) is introduced to enable the seismic fragility estimation of the component bridges, considering the uncertainty of earthquake locations and magnitudes. This is systemically used to carry out a post-hazard bridge network flow analysis by employing the matrix-based framework. Secondly, two different network performance measures are used to quantify the network performance after a seismic event. Maximum flow capacity was originally used for a bridge transportation network, however the numerical example using this measure is further developed for applications to more accurate system performance analysis using total system travel time (TSTT). Finally, a new method for system-level seismic risk assessment is proposed to carry out a bridge network flow analysis based on TSTT by employing the matrix-based system reliability (MSR) method. In the proposed method, the artificial neuron network (ANN) is introduced to approximate the network performance, which can reduce the computational cost of network analysis. The proposed method can provide statistical moments of the network performance and component importance measures, which can be used by decision-makers to reduce the seismic risk of a target area. The proposed method is tested by application to a numerical example of an actual transportation network in South Korea. In the seismic risk assessment of the example, PSHA is successfully integrated with the matrix-based framework to perform system reliability analysis in a computationally efficient manner.clos