Report (M.S.)--Virginia Polytechnic Institute and State University, 1994. / Includes bibliographical references (leaf 40). Also available via the Internet

Project report (M.S.)--Virginia Polytechnic Institute and State University, 1990. / Includes bibliographical references (leaves 164-166). Also available via the Internet.

Thesis (M.S.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 25-26). Also available via the Internet.

An important characteristic of all systems is availability. Availability is the probability that a system or piece of equipment will operate in a prescribed manner when used under specified conditions. It is primarily a design dependent parameter. Availability derives from a systems reliability and maintainability. Reliability is the probability that a system will operate for a specified time under specified operating conditions. It is commonly measured by the mean-time-between-failure (MTBF). Maintainability is the ability of a system to be maintained. For this report, maintainability is measured by the mean maintenance down time (MDT). The MDT is a function of several variables, including the mean-time-to-repair (MTIR) and Logistics Delay Time (LDT). MTIR is the time that active maintenance is being performed. The LDT is the time delay due to spare part availability, transportation, repair facility availability, traveling to the location of the malfunction, etc. LDT is a major portion of the MDT. In order to meet a requirement of improved system availability, the MTBF and/or the MDT must be improved. Decreasing the distance between the repair organization and the location of the failure may have a significant impact on LDT, assuming that the system's LDT is positively correlated to the distance traveled. An improvement in the LDT corresponds to an improvement in the MDT, hence the availability. For a situation where the repair function (referred to as the repair unit) travels to the location of the failed component, the deployment location may have a critical impact on a system's availability. In a system where the operating components are located over a wide area and the repair organization must travel to the component to effect a repair, there are numerous ways to deploy the servicing units. The systems maintenance concept addresses this issue. The deployment of the repair units is the central focus of this report. / Master of Science

The use of availability measures is very informative when analyzing the performance of repairable components. The derivation and evaluation of such measures is usually focused on describing the status of the component over time. It is not generally acknowledged that the resulting availability measure is in fact an expected value with respect to frequency. In a population of n independent, identical components, the number functioning at any point in time is a random variable. The distribution of this random variable is determined and described here. The intuitive view that this frequency distribution is binomial is verified. This is accomplished using direct analysis and Monte Carlo simulation. Two general cases are considered: (1) components with exponential life and repair time distributions, and (2) components with Weibull life distributions and exponential repair time distributions. The analysis leads to more accurate models of component behavior in terms of frequency including an exact confidence bound on the number of components functioning at any point in time. In addition, the time evolution of the frequency distribution is described and the relationships between the frequency distribution and the life and repair time distribution parameters are explored. Finally, the implications for availability decision analyses are shown. The overall result is a new perspective on availability which should prove to be very useful. / Master of Science

<p>Osnovni cilj ove doktorske disertacije bio je da se razvije novi automatizovani dijagnostiĉki model. Zatim da se praktiĉnom primenom tog modela (na konkretnom primeru automatizovane dijagnostike vitalnih komponenti tehniĉkih sistema u realnim pogonskim uslovima razliĉitih grana industrije) tehno-ekonomskom analizom potvrdi glavna hipoteza disertacije: &bdquo;Automatizovana dijagnostika tehniĉkih sistema u industriji (bazirana na kori&scaron;ćenju raĉunara i drugih savremenih informaciono-komunikacionih tehnologija) doprinosi porastu nivoau pouzdanosti i raspoloţivosti tih sistema, kao smanjenju ukupnih tro&scaron;kova i ekonomiĉnijem poslovanju preduzeća―.</p> / <p>The main objective of this doctoral thesis was to develop a new automated diagnostic model. Then with the practical usage of this model (in particular case of automated diagnostics vital components of technical systems in real operating conditions of different industries) with techno-economic analysis to confirm the main hypothesis of the dissertation: ―Automated diagnostics of technical systems in the industry (based on the use of computers and other modern information and communication technologies) contributes to the increase levels of reliability and availability of these systems, as well as the reduction of total costs and more economical management of the company‖.</p>

A Sustainable Autonomic Architecture for Organically Reconfigurable Computing System based on SRAM Field Programmable Gate Arrays (FPGAs) is proposed, modeled analytically, simulated, prototyped, and measured. Low-level organic elements are analyzed and designed to achieve novel self-monitoring, self-diagnosis, and self-repair organic properties. The prototype of a 2-D spatial gradient Sobel video edge-detection organic system use-case developed on a XC4VSX35 Xilinx Virtex-4 Video Starter Kit is presented. Experimental results demonstrate the applicability of the proposed architecture and provide the infrastructure to quantify the performance and overcome fault-handling limitations. Dynamic online autonomous functionality restoration after a malfunction or functionality shift due to changing requirements is achieved at a fine granularity by exploiting dynamic Partial Reconfiguration (PR) techniques. A Genetic Algorithm (GA)-based hardware/software platform for intrinsic evolvable hardware is designed and evaluated for digital circuit repair using a variety of well-accepted benchmarks. Dynamic bitstream compilation for enhanced mutation and crossover operators is achieved by directly manipulating the bitstream using a layered toolset. Experimental results on the edge-detector organic system prototype have shown complete organic online refurbishment after a hard fault. In contrast to previous toolsets requiring many milliseconds or seconds, an average of 0.47 microseconds is required to perform the genetic mutation, 4.2 microseconds to perform the single point conventional crossover, 3.1 microseconds to perform Partial Match Crossover (PMX) as well as Order Crossover (OX), 2.8 microseconds to perform Cycle Crossover (CX), and 1.1 milliseconds for one input pattern intrinsic evaluation. These represent a performance advantage of three orders of magnitude over the JBITS software framework and more than seven orders of magnitude over the Xilinx design flow. Combinatorial Group Testing (CGT) technique was combined with the conventional GA in what is called CGT-pruned GA to reduce repair time and increase system availability. Results have shown up to 37.6% convergence advantage using the pruned technique. Lastly, a quantitative stochastic sustainability model for reparable systems is formulated to evaluate the Sustainability of FPGA-based reparable systems. This model computes at design-time the resources required for refurbishment to meet mission availability and lifetime requirements in a given fault-susceptible missions. By applying this model to MCNC benchmark circuits and the Sobel Edge-Detector in a realistic space mission use-case on Xilinx Virtex-4 FPGA, we demonstrate a comprehensive model encompassing the inter-relationships between system sustainability and fault rates, utilized, and redundant hardware resources, repair policy parameters and decaying reparability.

It was not long ago. On Christmas Eve 2012, a war of troubleshooting began in Amazon data centers. It started at 12:24 PM, with an mistaken deletion of the state data of Amazon Elastic Load Balancing Service (ELB for short), which was not realized at that time. The mistake first led to a local issue that a small number of ELB service APIs were affected. In about six minutes, it evolved into a critical one that EC2 customers were significantly affected. One example was that Netflix, which was using hundreds of Amazon ELB services, was experiencing an extensive streaming service outage when many customers could not watch TV shows or movies on Christmas Eve. It took Amazon engineers 5 hours 42 minutes to find the root cause, the mistaken deletion, and another 15 hours and 32 minutes to fully recover the ELB service. The war ended at 8:15 AM the next day and brought the performance troubleshooting in data centers to world’s attention. As shown in this Amazon ELB case.Troubleshooting runtime performance issues is crucial in time-sensitive multi-tier cloud services because of their stringent end-to-end timing requirements, but it is also notoriously difficult and time consuming. To address the troubleshooting challenge, this dissertation proposes VScope, a flexible monitoring and analysis system for online troubleshooting in data centers. VScope provides primitive operations which data center operators can use to troubleshoot various performance issues. Each operation is essentially a series of monitoring and analysis functions executed on an overlay network. We design a novel software architecture for VScope so that the overlay networks can be generated, executed and terminated automatically, on-demand. From the troubleshooting side, we design novel anomaly detection algorithms and implement them in VScope. By running anomaly detection algorithms in VScope, data center operators are notified when performance anomalies happen. We also design a graph-based guidance approach, called VFocus, which tracks the interactions among hardware and software components in data centers. VFocus provides primitive operations by which operators can analyze the interactions to find out which components are relevant to the performance issue. VScope’s capabilities and performance are evaluated on a testbed with over 1000 virtual machines (VMs). Experimental results show that the VScope runtime negligibly perturbs system and application performance, and requires mere seconds to deploy monitoring and analytics functions on over 1000 nodes. This demonstrates VScope’s ability to support fast operation and online queries against a comprehensive set of application to system/platform level metrics, and a variety of representative analytics functions. When supporting algorithms with high computation complexity, VScope serves as a ‘thin layer’ that occupies no more than 5% of their total latency. Further, by using VFocus, VScope can locate problematic VMs that cannot be found via solely application-level monitoring, and in one of the use cases explored in the dissertation, it operates with levels of perturbation of over 400% less than what is seen for brute-force and most sampling-based approaches. We also validate VFocus with real-world data center traces. The experimental results show that VFocus has troubleshooting accuracy of 83% on average.

This thesis work is performed in collaboration with Syntell AB and a client company interested in assistance with charging infrastructure dimensioning. The aim of this thesis is to develop an executable, generalizable model that can aid decision making regarding charging infrastructure. Furthermore, this is done within a Model-Based Systems Engineering (MBSE) context, which enables representation of the system as a model.  As the data and model concerning the client company is classified, it is not presented in this report. Instead, to further enhance the aim of developing a generalizable model, a test case is produced for this project work. This case consists of passenger electric vehicles and chargers in a metropolitan setting, where data is gathered from public sources.  The results show that the model is executable and flexible to fit any type of electric vehicle and different specifications of chargers. Using an MBSE approach enables the project owner to customize the model development for the specific use case. Additionally, defining a system in focus establishes what the system uptime is, enabling calculations of availability. The results for this specific use case are interpreted to show how the model can be used to aid the dimensioning of charging infrastructure using the model output. To further verify the model representation of the system, the model can be run in live-mode, where vehicles and chargers can be added while the model is running to instantly examine the system dynamics.  Concluding, the method for utilizing the model to evaluate systems availability is described. The model output, as well as the thorough description of the model, can be used to increase the knowledge within MBSE for executable modeling. / Detta projekt utförs i samarbete med Syntell AB och en tredje part som är intresserad av att förbättra sitt beslutsunderlag gällande dimensionering av laddinfrastruktur. Målet med arbetet är att utveckla en exekverbar och generaliserbar modell som kan användas för att underlätta dimensionering av laddinfrastruktur. Vidare så genomförs arbetet inom ramen för MBSE, vilket möjliggör en representation av systemet med hjälp av en modell. Eftersom kunddatan är konfidentiell appliceras modellen på ett fabricerat användningsfall som underlag för denna rapport. Detta baserar sig på elektriska personbilar i stadsmiljö, där data hämtas från publika källor. Att modellen används på två olika användningsfall stärker argumenten kring generaliserbarhet. Resultatet som presenteras är främst i syfte att demonstrera tolkning av resultat samt att grundligt förklara modellen. Resultaten visar att modellen är exekverbar och flexibel för att kunna anpassas utifrån godtyckligt elektriskt fordon och olika specifikationer för laddstationer. Genom att definiera ett system i fokus kan systemets drifttid utgöra grund för att kunna beräkna tillgänglighet. Resultaten tolkas i det specifika användningsfallet för att illustrera hur modellen kan användas för att underlätta en beslutsprocess gällande dimensionering av laddinfrastruktur. För att ytterligare verifiera modellens representation av verkligheten används ett interaktivt live-mode, där fordon och laddare kan läggas till under körning för att direkt undersöka systemdynamiken. Detta är ett viktigt verktyg vid kommunikation med intressenter för att stärka förtroendet för simuleringsresultat.  Sammanfattningsvis beskrivs metoden för att använda modellen till att undersöka systemtillgänglighet. Modellens utdata, tillsammans med beskrivningen av modellen, kan användas för att utöka kunskapen inom MBSE för exekverbar modellering.

Ces travaux s'intéressent à l'estimation de la fiabilité des évacuateurs de crues vannés. Le comportement fiabilistes de ces systèmes hydrauliques dépend à la fois d'événements aléatoires discrets, mais aussi de l'évolution d'une variable déterministe continue : ce sont des systèmes dynamiques hybrides. Pour ces systèmes, l'événement redouté est réalisé lorsque le niveau de la retenue atteint un seuil de sûreté. La démarche de fiabilité dynamique proposée dans cette thèse vise à prendre en compte l’information temporelle de la modélisation à la synthèse d'indicateurs fiabilistes pour l'aide à la décision et développe deux contributions :1) L'élaboration d'une base de connaissances dédiée à la description des évacuateurs de crue en termes de fiabilité dynamique. Chaque classe de composants est décrite par un automate stochastique hybride dont les états sont les différentes phases de son fonctionnement. 2) Le suivi de la simulation de Monte Carlo et le traitement et l'analyse des "histoires" (séquence de tous les états activés et des dates d'activation) obtenues en simulation pour construire des indicateurs de fiabilité classique (probabilité d'occurrence de l'évènement redouté, identification des coupes équivalentes prépondérantes, ...). Des indicateurs de fiabilité dynamique basés sur la classification des histoires en fonction des dates de défaillance des composants concernés et sur l'estimation de l'importance dynamique sont aussi proposés / Hydraulic systems are hybrid dynamic systems whose evolution is a combination between discrete stochastic events on the one hand and continuous deterministic phenomena on the other hand. The undesired event is achieved when the dam level reaches a security threshold. In the frame of gated spillways dynamic reliability, the proposed methodology takes into account the temporal information during modeling and synthesis of reliability indicators for decision support.The first contribution of this work is the development of a knowledge base to describe a class of systems. Each component is described by a stochastic hybrid automaton whose states are the different working modes.The second contribution is Monte Carlo simulation monitoring and treatment results. A story is the sequence of all activated states and activation dates during the algorithm passage for a simulation. The analysis of results provides classical reliability indicators, such as the time evolution of the undesired event probability or the identification of predominant equivalent cuts. Our predictive approach is based on stories classification depending on components failure dates, then dynamic importance is assessed