Синтез дискретных устройств методом последовательной декомпозиции автоматных моделей

Приводиться метод синтезу дискретних пристроїв, якій грунтується на застосуванні послідовної декомпозіції автоматних моделей пристроїв. Обгрунтовані необхідні та достатні умови, що забезпечують реалізацію послідовної декомпозіції составного автомата. Показана перевага приведеного методу синтезу перед відомими методами проектування цифрових пристроїв.Finite state machines are widely used to model systems in diverse areas. Often, the modeling machines can be decomposed into smaller component machines and this decomposition can facilitate the system design, implementation and analysis. In this paper, principles of serial (cascade) decomposition that is based on the closed partition of the internal states of the composite of the digital circuits by using properties of serial decomposition of machines

Remarks on strong stabilization and stable H∞ controller design

Cataloged from PDF version of article.A state space based design method is given to find strongly stabilizing controllers for multi-input-multi-output plants (MIMO). A sufficient condition is derived for the existence of suboptimal stable H∞ controller in terms of linear matrix inequalities (LMI) and the controller order is twice that of the plant A new parameterization of strongly stabilizing controllers is determined using linear fractional transformations (LFT)

The effect of the distributed test architecture on the power of testing

Copyright @ 2008 Oxford University PressThere has been much interest in testing from finite-state machines (FSMs). If the system under test can be modelled by the (minimal) FSM N then testing from an (minimal) FSM M is testing to check that N is isomorphic to M. In the distributed test architecture, there are multiple interfaces/ports and there is a tester at each port. This can introduce controllability/synchronization and observability problems. This paper shows that the restriction to test sequences that do not cause controllability problems and the inability to observe the global behaviour in the distributed test architecture, and thus relying only on the local behaviour at remote testers, introduces fundamental limitations into testing. There exist minimal FSMs that are not equivalent, and so are not isomorphic, and yet cannot be distinguished by testing in this architecture without introducing controllability problems. Similarly, an FSM may have non-equivalent states that cannot be distinguished in the distributed test architecture without causing controllability problems: these are said to be locally s-equivalent and otherwise they are locally s-distinguishable. This paper introduces the notion of two states or FSMs being locally s-equivalent and formalizes the power of testing in the distributed test architecture in terms of local s-equivalence. It introduces a polynomial time algorithm that, given an FSM M, determines which states of M are locally s-equivalent and produces minimal length input sequences that locally s-distinguish states that are not locally s-equivalent. An FSM is locally s-minimal if it has no pair of locally s-equivalent states. This paper gives an algorithm that takes an FSM M and returns a locally s-minimal FSM M′ that is locally s-equivalent to M.This work was supported in part by Leverhulme Trust grant number F/00275/D, Testing State Based Systems, Natural Sciences and Engineering Research Council (NSERC) of Canada grant number RGPIN 976, and Engineering and Physical Sciences Research Council grant number GR/R43150, Formal Methods and Testing (FORTEST)

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

A Framework for Supporting Intelligent Fault and Performance Management for Communication Networks

In this paper, we present a framework for supporting intelligent fault and performance management for communication networks. Belief networks are taken as the basis for knowledge representation and inference under evidence. When using belief networks for diagnosis, we identify two questions: When can I say that I get the right diagnosis and stop? If right diagnosis has not been obtained yet, which test should I choose next? For the first question, we define the notion of right diagnosis via the introduction of intervention networks. For the second question, we formulate the decision making procedure using the framework of partially observable Markov decision processes. A heuristic dynamic strategy is proposed to solve this problem and the effectiveness is shown via simulation

Network anomaly detection using management information base (MIB) network traffic variables

In this dissertation, a hierarchical, multi-tier, multiple-observation-window, network anomaly detection system (NADS) is introduced, namely, the MIB Anomaly Detection (MAD) system, which is capable of detecting and diagnosing network anomalies (including network faults and Denial of Service computer network attacks) proactively and adaptively. The MAD system utilizes statistical models and neural network classifier to detect network anomalies through monitoring the subtle changes of network traffic patterns. The process of measuring network traffic pattern is achieved by monitoring the Management Information Base (Mifi) II variables, supplied by the Simple Network Management Protocol (SNMP) LI. The MAD system then converted each monitored Mifi variable values, collected during each observation window, into a Probability Density Function (PDF), processed them statistically, combined intelligently the result for each individual variable and derived the final decision. The MAD system has a distributed, hierarchical, multi-tier architecture, based on which it could provide the health status of each network individual element. The inter-tier communication requires low network bandwidth, thus, making it possibly utilization on capacity challenged wireless as well as wired networks. Efficiently and accurately modeling network traffic behavior is essential for building NADS. In this work, a novel approach to statistically model network traffic measurements with high variability is introduced, that is, dividing the network traffic measurements into three different frequency segments and modeling the data in each frequency segment separately. Also in this dissertation, a new network traffic statistical model, i.e., the one-dimension hyperbolic distribution, is introduced

A Hierarchical Filtering-Based Monitoring Architecture for Large-scale Distributed Systems

On-line monitoring is essential for observing and improving the reliability and performance of large-scale distributed (LSD) systems. In an LSD environment, large numbers of events are generated by system components during their execution and interaction with external objects (e.g. users or processes). These events must be monitored to accurately determine the run-time behavior of an LSD system and to obtain status information that is required for debugging and steering applications. However, the manner in which events are generated in an LSD system is complex and represents a number of challenges for an on-line monitoring system. Correlated events axe generated concurrently and can occur at multiple locations distributed throughout the environment. This makes monitoring an intricate task and complicates the management decision process. Furthermore, the large number of entities and the geographical distribution inherent with LSD systems increases the difficulty of addressing traditional issues, such as performance bottlenecks, scalability, and application perturbation. This dissertation proposes a scalable, high-performance, dynamic, flexible and non-intrusive monitoring architecture for LSD systems. The resulting architecture detects and classifies interesting primitive and composite events and performs either a corrective or steering action. When appropriate, information is disseminated to management applications, such as reactive control and debugging tools. The monitoring architecture employs a novel hierarchical event filtering approach that distributes the monitoring load and limits event propagation. This significantly improves scalability and performance while minimizing the monitoring intrusiveness. The architecture provides dynamic monitoring capabilities through: subscription policies that enable applications developers to add, delete and modify monitoring demands on-the-fly, an adaptable configuration that accommodates environmental changes, and a programmable environment that facilitates development of self-directed monitoring tasks. Increased flexibility is achieved through a declarative and comprehensive monitoring language, a simple code instrumentation process, and automated monitoring administration. These elements substantially relieve the burden imposed by using on-line distributed monitoring systems. In addition, the monitoring system provides techniques to manage the trade-offs between various monitoring objectives. The proposed solution offers improvements over related works by presenting a comprehensive architecture that considers the requirements and implied objectives for monitoring large-scale distributed systems. This architecture is referred to as the HiFi monitoring system. To demonstrate effectiveness at debugging and steering LSD systems, the HiFi monitoring system has been implemented at the Old Dominion University for monitoring the Interactive Remote Instruction (IRI) system. The results from this case study validate that the HiFi system achieves the objectives outlined in this thesis

Fault Detection and Identification in Computer Networks: A soft Computing Approach

Governmental and private institutions rely heavily on reliable computer networks for their everyday business transactions. The downtime of their infrastructure networks may result in millions of dollars in cost. Fault management systems are used to keep today’s complex networks running without significant downtime cost, either by using active techniques or passive techniques. Active techniques impose excessive management traffic, whereas passive techniques often ignore uncertainty inherent in network alarms,leading to unreliable fault identification performance. In this research work, new algorithms are proposed for both types of techniques so as address these handicaps. Active techniques use probing technology so that the managed network can be tested periodically and suspected malfunctioning nodes can be effectively identified and isolated. However, the diagnosing probes introduce extra management traffic and storage space. To address this issue, two new CSP (Constraint Satisfaction Problem)-based algorithms are proposed to minimize management traffic, while effectively maintain the same diagnostic power of the available probes. The first algorithm is based on the standard CSP formulation which aims at reducing the available dependency matrix significantly as means to reducing the number of probes. The obtained probe set is used for fault detection and fault identification. The second algorithm is a fuzzy CSP-based algorithm. This proposed algorithm is adaptive algorithm in the sense that an initial reduced fault detection probe set is utilized to determine the minimum set of probes used for fault identification. Based on the extensive experiments conducted in this research both algorithms have demonstrated advantages over existing methods in terms of the overall management traffic needed to successfully monitor the targeted network system. Passive techniques employ alarms emitted by network entities. However, the fault evidence provided by these alarms can be ambiguous, inconsistent, incomplete, and random. To address these limitations, alarms are correlated using a distributed Dempster-Shafer Evidence Theory (DSET) framework, in which the managed network is divided into a cluster of disjoint management domains. Each domain is assigned an Intelligent Agent for collecting and analyzing the alarms generated within that domain. These agents are coordinated by a single higher level entity, i.e., an agent manager that combines the partial views of these agents into a global one. Each agent employs DSET-based algorithm that utilizes the probabilistic knowledge encoded in the available fault propagation model to construct a local composite alarm. The Dempster‘s rule of combination is then used by the agent manager to correlate these local composite alarms. Furthermore, an adaptive fuzzy DSET-based algorithm is proposed to utilize the fuzzy information provided by the observed cluster of alarms so as to accurately identify the malfunctioning network entities. In this way, inconsistency among the alarms is removed by weighing each received alarm against the others, while randomness and ambiguity of the fault evidence are addressed within soft computing framework. The effectiveness of this framework has been investigated based on extensive experiments. The proposed fault management system is able to detect malfunctioning behavior in the managed network with considerably less management traffic. Moreover, it effectively manages the uncertainty property intrinsically contained in network alarms,thereby reducing its negative impact and significantly improving the overall performance of the fault management system

Communication between nodes for autonomic and distributed management

Doutoramento conjunto MAPi em InformáticaOver the last decade, the most widespread approaches for traditional management were based on the Simple Network Management Protocol (SNMP) or Common Management Information Protocol (CMIP). However, they both have several problems in terms of scalability, due to their centralization characteristics. Although the distributed management approaches exhibit better performance in terms of scalability, they still underperform regarding communication costs, autonomy, extensibility, exibility, robustness, and cooperation between network nodes. The cooperation between network nodes normally requires excessive overheads for synchronization and dissemination of management information in the network. For emerging dynamic and large-scale networking environments, as envisioned in Next Generation Networks (NGNs), exponential growth in the number of network devices and mobile communications and application demands is expected. Thus, a high degree of management automation is an important requirement, along with new mechanisms that promote it optimally and e ciently, taking into account the need for high cooperation between the nodes. Current approaches for self and autonomic management allow the network administrator to manage large areas, performing fast reaction and e ciently facing unexpected problems. The management functionalities should be delegated to a self-organized plane operating within the network, that decrease the network complexity and the control information ow, as opposed to centralized or external servers. This Thesis aims to propose and develop a communication framework for distributed network management which integrates a set of mechanisms for initial communication, exchange of management information, network (re) organization and data dissemination, attempting to meet the autonomic and distributed management requirements posed by NGNs. The mechanisms are lightweight and portable, and they can operate in di erent hardware architectures and include all the requirements to maintain the basis for an e cient communication between nodes in order to ensure autonomic network management. Moreover, those mechanisms were explored in diverse network conditions and events, such as device and link errors, di erent tra c/network loads and requirements. The results obtained through simulation and real experimentation show that the proposed mechanisms provide a lower convergence time, smaller overhead impact in the network, faster dissemination of management information, increase stability and quality of the nodes associations, and enable the support for e cient data information delivery in comparison to the base mechanisms analyzed. Finally, all mechanisms for communication between nodes proposed in this Thesis, that support and distribute the management information and network control functionalities, were devised and developed to operate in completely decentralized scenarios.Durante a última década, protocolos como Simple Network Management Protocol (SNMP) ou Common Management Information Protocol (CMIP) foram as abordagens mais comuns para a gestão tradicional de redes. Essas abordagens têm vários problemas em termos de escalabilidade, devido às suas características de centralização. Apresentando um melhor desempenho em termos de escalabilidade, as abordagens de gestão distribuída, por sua vez, são vantajosas nesse sentido, mas também apresentam uma série de desvantagens acerca do custo elevado de comunicação, autonomia, extensibilidade, exibilidade, robustez e cooperação entre os nós da rede. A cooperação entre os nós presentes na rede é normalmente a principal causa de sobrecarga na rede, uma vez que necessita de colectar, sincronizar e disseminar as informações de gestão para todos os nós nela presentes. Em ambientes dinâmicos, como é o caso das redes atuais e futuras, espera-se um crescimento exponencial no número de dispositivos, associado a um grau elevado de mobilidade dos mesmos na rede. Assim, o grau elevado de funções de automatiza ção da gestão da rede é uma exigência primordial, bem como o desenvolvimento de novos mecanismos e técnicas que permitam essa comunicação de forma optimizada e e ciente. Tendo em conta a necessidade de elevada cooperação entre os elementos da rede, as abordagens atuais para a gestão autonómica permitem que o administrador possa gerir grandes áreas de forma rápida e e ciente frente a problemas inesperados, visando diminuir a complexidade da rede e o uxo de informações de controlo nela gerados. Nas gestões autonómicas a delegação de operações da rede é suportada por um plano auto-organizado e não dependente de servidores centralizados ou externos. Com base nos tipos de gestão e desa os acima apresentados, esta Tese tem como principal objetivo propor e desenvolver um conjunto de mecanismos necessários para a criação de uma infra-estrutura de comunicação entre nós, na tentativa de satisfazer as exigências da gestão auton ómica e distribuída apresentadas pelas redes de futura geração. Nesse sentido, mecanismos especí cos incluindo inicialização e descoberta dos elementos da rede, troca de informação de gestão, (re) organização da rede e disseminação de dados foram elaborados e explorados em diversas condições e eventos, tais como: falhas de ligação, diferentes cargas de tráfego e exigências de rede. Para além disso, os mecanismos desenvolvidos são leves e portáveis, ou seja, podem operar em diferentes arquitecturas de hardware e contemplam todos os requisitos necessários para manter a base de comunicação e ciente entre os elementos da rede. Os resultados obtidos através de simulações e experiências reais comprovam que os mecanismos propostos apresentam um tempo de convergência menor para descoberta e troca de informação, um menor impacto na sobrecarga da rede, disseminação mais rápida da informação de gestão, aumento da estabilidade e a qualidade das ligações entre os nós e entrega e ciente de informações de dados em comparação com os mecanismos base analisados. Finalmente, todos os mecanismos desenvolvidos que fazem parte da infrastrutura de comunicação proposta foram concebidos e desenvolvidos para operar em cenários completamente descentralizados