Optimizing recovery protocols for replicated database systems

En la actualidad, el uso de tecnologías de informacíon y sistemas de cómputo tienen una gran influencia en la vida diaria. Dentro de los sistemas informáticos actualmente en uso, son de gran relevancia los sistemas distribuidos por la capacidad que pueden tener para escalar, proporcionar soporte para la tolerancia a fallos y mejorar el desempeño de aplicaciones y proporcionar alta disponibilidad. Los sistemas replicados son un caso especial de los sistemas distribuidos. Esta tesis está centrada en el área de las bases de datos replicadas debido al uso extendido que en el presente se hace de ellas, requiriendo características como: bajos tiempos de respuesta, alto rendimiento en los procesos, balanceo de carga entre las replicas, consistencia e integridad de datos y tolerancia a fallos. En este contexto, el desarrollo de aplicaciones utilizando bases de datos replicadas presenta dificultades que pueden verse atenuadas mediante el uso de servicios de soporte a mas bajo nivel tales como servicios de comunicacion y pertenencia. El uso de los servicios proporcionados por los sistemas de comunicación de grupos permiten ocultar los detalles de las comunicaciones y facilitan el diseño de protocolos de replicación y recuperación. En esta tesis, se presenta un estudio de las alternativas y estrategias empleadas en los protocolos de replicación y recuperación en las bases de datos replicadas. También se revisan diferentes conceptos sobre los sistemas de comunicación de grupos y sincronia virtual. Se caracterizan y clasifican diferentes tipos de protocolos de replicación con respecto a la interacción o soporte que pudieran dar a la recuperación, sin embargo el enfoque se dirige a los protocolos basados en sistemas de comunicación de grupos. Debido a que los sistemas comerciales actuales permiten a los programadores y administradores de sistemas de bases de datos renunciar en alguna medida a la consistencia con la finalidad de aumentar el rendimiento, es importante determinar el nivel de consistencia necesario. En el caso de las bases de datos replicadas la consistencia está muy relacionada con el nivel de aislamiento establecido entre las transacciones. Una de las propuestas centrales de esta tesis es un protocolo de recuperación para un protocolo de replicación basado en certificación. Los protocolos de replicación de base de datos basados en certificación proveen buenas bases para el desarrollo de sus respectivos protocolos de recuperación cuando se utiliza el nivel de aislamiento snapshot. Para tal nivel de aislamiento no se requiere que los readsets sean transferidos entre las réplicas ni revisados en la fase de cetificación y ya que estos protocolos mantienen un histórico de la lista de writesets que es utilizada para certificar las transacciones, este histórico provee la información necesaria para transferir el estado perdido por la réplica en recuperación. Se hace un estudio del rendimiento del protocolo de recuperación básico y de la versión optimizada en la que se compacta la información a transferir. Se presentan los resultados obtenidos en las pruebas de la implementación del protocolo de recuperación en el middleware de soporte. La segunda propuesta esta basada en aplicar el principio de compactación de la informacion de recuperación en un protocolo de recuperación para los protocolos de replicación basados en votación débil. El objetivo es minimizar el tiempo necesario para transfeir y aplicar la información perdida por la réplica en recuperación obteniendo con esto un protocolo de recuperación mas eficiente. Se ha verificado el buen desempeño de este algoritmo a través de una simulación. Para efectuar la simulación se ha hecho uso del entorno de simulación Omnet++. En los resultados de los experimentos puede apreciarse que este protocolo de recuperación tiene buenos resultados en múltiples escenarios. Finalmente, se presenta la verificación de la corrección de ambos algoritmos de recuperación en el Capítulo 5.Nowadays, information technology and computing systems have a great relevance on our lives. Among current computer systems, distributed systems are one of the most important because of their scalability, fault tolerance, performance improvements and high availability. Replicated systems are a specific case of distributed system. This Ph.D. thesis is centered in the replicated database field due to their extended usage, requiring among other properties: low response times, high throughput, load balancing among replicas, data consistency, data integrity and fault tolerance. In this scope, the development of applications that use replicated databases raises some problems that can be reduced using other fault-tolerant building blocks, as group communication and membership services. Thus, the usage of the services provided by group communication systems (GCS) hides several communication details, simplifying the design of replication and recovery protocols. This Ph.D. thesis surveys the alternatives and strategies being used in the replication and recovery protocols for database replication systems. It also summarizes different concepts about group communication systems and virtual synchrony. As a result, the thesis provides a classification of database replication protocols according to their support to (and interaction with) recovery protocols, always assuming that both kinds of protocol rely on a GCS. Since current commercial DBMSs allow that programmers and database administrators sacrifice consistency with the aim of improving performance, it is important to select the appropriate level of consistency. Regarding (replicated) databases, consistency is strongly related to the isolation levels being assigned to transactions. One of the main proposals of this thesis is a recovery protocol for a replication protocol based on certification. Certification-based database replication protocols provide a good basis for the development of their recovery strategies when a snapshot isolation level is assumed. In that level readsets are not needed in the validation step. As a result, they do not need to be transmitted to other replicas. Additionally, these protocols hold a writeset list that is used in the certification/validation step. That list maintains the set of writesets needed by the recovery protocol. This thesis evaluates the performance of a recovery protocol based on the writeset list tranfer (basic protocol) and of an optimized version that compacts the information to be transferred. The second proposal applies the compaction principle to a recovery protocol designed for weak-voting replication protocols. Its aim is to minimize the time needed for transferring and applying the writesets lost by the recovering replica, obtaining in this way an efficient recovery. The performance of this recovery algorithm has been checked implementing a simulator. To this end, the Omnet++ simulating framework has been used. The simulation results confirm that this recovery protocol provides good results in multiple scenarios. Finally, the correction of both recovery protocols is also justified and presented in Chapter 5.García Muñoz, LH. (2013). Optimizing recovery protocols for replicated database systems [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/31632TESI

New Concepts for Virtual Testbeds : Data Mining Algorithms for Blackbox Optimization based on Wait-Free Concurrency and Generative Simulation

Virtual testbeds have emerged as a key technology for improving and streamlining complex engineering processes by delivering long-term simulation and assessment of complex designs in virtual environments. In contrast to existing simulation technology, virtual testbeds focus on long-term physically-based simulation of the overall design in its (virtual) environment instead of only focussing on isolated, specific parts for short periods of time. This technology has the major advantage that costly testing, prototyping, and assessment in real-life environments are replaced by a cost-efficient simulation in virtual worlds for comprehensive and long-term analysis of designs. For this purpose, engineering models and their requirements are abstracted into software simulation models and objectives which are executed in virtual assessments. Simulation models are used to predict complex, real systems which can be further a subject to random influences. These predictions are used to examine the effects of individual configuration alternatives without actually realizing them and causing possible negative effects on the real system. Virtual testbeds further offer engineers the opportunity to immersively and naturally interact with their simulation model in these virtual assessments. This enables a greater and comprehensive understanding of possible design flaws early-on in the design process for engineers because they can directly assess their design in the virtual environment, based on the simulation objectives. The fact that virtual testbeds enable these realtime interactive virtual assessments, makes their underlying software infrastructure very complex. One major challenge is to minimize the development time of virtual testbeds in order to efficiently integrate them into the overall engineering process. Usually, this can be achieved by minimizing the underlying concurrency of the testbed and by simplifying its software architecture. However, this may result in a degradation of their very concurrent and asynchronous behavior, which is usually required for immersive and natural virtual interaction. A major goal of virtual testbeds in the engineering process is to find a set of optimal configurations of the simulation model which maximizes all simulation objectives for the specified virtual assessments. Once such a set has been computed, engineers can interactively explore it in the virtual environment. The main challenge is that sophisticated simulation models and their configuration are subject to a multiobjective optimization problem, which usually can not be solved manually by engineers or simulation analysts in feasible time. This is further aggravated because the relationships between simulation model configurations and simulation objectives are mostly unknown, leading to what is known as blackbox simulations. In this thesis, I propose novel data mining algorithms for computing Pareto optimal simulation model configurations, based on an approximation of the feasible design space, for deterministic and stochastic blackbox simulations in virtual testbeds for achieving above stated goal. These novel data mining algorithms lead to an automatic knowledge discovery process that does not need any supervision for its data analysis and assessment for multiobjective optimization problems of simulation model configurations. This achieves the previously stated goal of computing optimal configurations of simulation models for long-term simulations and assessments. Furthermore, I propose two complementary solutions for efficiently integrating massively-parallel virtual testbeds into engineering processes. First, I propose a novel multiversion wait-free data and concurrency management based on hash maps. These wait-free hash maps do not require any standard locking mechanisms and enable low-latency data generation, management and distribution for massively-parallel applications. Second, I propose novel concepts for efficiently code generating above wait-free data and concurrency management for arbitrary massively-parallel simulation applications of virtual testbeds. My generative simulation concept combines a state-of-the-art realtime interactive system design pattern for high maintainability with template code generation based on domain specific modelling. This concept is able to generate massively-parallel simulations and, at the same time, model checks its internal dataflow for possible interface errors. These generative concept overcomes the challenge of efficiently integrating virtual testbeds into engineering processes. These contributions enable for the first time a powerful collaboration between simulation, optimization, visualization and data analysis for novel virtual testbed applications but also overcome and achieve the presented challenges and goals

Formal design of data warehouse and OLAP systems : a dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Information Systems at Massey University, Palmerston North, New Zealand

A data warehouse is a single data store, where data from multiple data sources is integrated for online business analytical processing (OLAP) of an entire organisation. The rationale being single and integrated is to ensure a consistent view of the organisational business performance independent from different angels of business perspectives. Due to its wide coverage of subjects, data warehouse design is a highly complex, lengthy and error-prone process. Furthermore, the business analytical tasks change over time, which results in changes in the requirements for the OLAP systems. Thus, data warehouse and OLAP systems are rather dynamic and the design process is continuous. In this thesis, we propose a method that is integrated, formal and application-tailored to overcome the complexity problem, deal with the system dynamics, improve the quality of the system and the chance of success. Our method comprises three important parts: the general ASMs method with types, the application tailored design framework for data warehouse and OLAP, and the schema integration method with a set of provably correct refinement rules. By using the ASM method, we are able to model both data and operations in a uniform conceptual framework, which enables us to design an integrated approach for data warehouse and OLAP design. The freedom given by the ASM method allows us to model the system at an abstract level that is easy to understand for both users and designers. More specifically, the language allows us to use the terms from the user domain not biased by the terms used in computer systems. The pseudo-code like transition rules, which gives the simplest form of operational semantics in ASMs, give the closeness to programming languages for designers to understand. Furthermore, these rules are rooted in mathematics to assist in improving the quality of the system design. By extending the ASMs with types, the modelling language is tailored for data warehouse with the terms that are well developed for data-intensive applications, which makes it easy to model the schema evolution as refinements in the dynamic data warehouse design. By providing the application-tailored design framework, we break down the design complexity by business processes (also called subjects in data warehousing) and design concerns. By designing the data warehouse by subjects, our method resembles Kimball's "bottom-up" approach. However, with the schema integration method, our method resolves the stovepipe issue of the approach. By building up a data warehouse iteratively in an integrated framework, our method not only results in an integrated data warehouse, but also resolves the issues of complexity and delayed ROI (Return On Investment) in Inmon's "top-down" approach. By dealing with the user change requests in the same way as new subjects, and modelling data and operations explicitly in a three-tier architecture, namely the data sources, the data warehouse and the OLAP (online Analytical Processing), our method facilitates dynamic design with system integrity. By introducing a notion of refinement specific to schema evolution, namely schema refinement, for capturing the notion of schema dominance in schema integration, we are able to build a set of correctness-proven refinement rules. By providing the set of refinement rules, we simplify the designers's work in correctness design verification. Nevertheless, we do not aim for a complete set due to the fact that there are many different ways for schema integration, and neither a prescribed way of integration to allow designer favored design. Furthermore, given its °exibility in the process, our method can be extended for new emerging design issues easily

Design Development Test and Evaluation (DDT and E) Considerations for Safe and Reliable Human Rated Spacecraft Systems

A team directed by the NASA Engineering and Safety Center (NESC) collected methodologies for how best to develop safe and reliable human rated systems and how to identify the drivers that provide the basis for assessing safety and reliability. The team also identified techniques, methodologies, and best practices to assure that NASA can develop safe and reliable human rated systems. The results are drawn from a wide variety of resources, from experts involved with the space program since its inception to the best-practices espoused in contemporary engineering doctrine. This report focuses on safety and reliability considerations and does not duplicate or update any existing references. Neither does it intend to replace existing standards and policy

A diagnostic expert system as a tool for technology improvement support

This dissertation focuses on design, modelling and development of a diagnostic expert system, which was implemented as a tool (named Capability Diagnostic) in FutureSME project (and web portal) as one of the tools for self-diagnostic of SMEs, where according to analysis of current state the output data were generated. These data were used for creation of an action plan, which serves as a list of improvements that need to be done to solve crucial processes of the company. After improvements in company processes were completed, a new diagnostic process was initiated and a comparison with previous results was performed. This tool was evaluated by companies partnering to the project as one of the most contributive results of the project. Design, modelling and development of the system were focused on general use of the diagnostic system for company processes and improvement support.Tato disertační práce se zabývá návrhem, modelováním, vývojem a realizací obecného diagnostického expertního systému, který byl jako nástroj (nazván jako Capability Diagnostic) nasazen v rámci projektu (a portálu) FutureSME jako jeden z nástrojů pro diagnostiku malých a středních podniků, kde na základě analýzy aktuálního stavu generoval výstupní data, která byla použita pro vytvoření akčního plánu, na základě kterého byly provedeny zásahy do chodu firmy, které měly napomoci k řešení klíčových procesů jejího fungování. Po zavedení opatření byla opakovaně provedena diagnostika a výsledek byl porovnán s původním či předchozím stavem. Tento nástroj byl partnery projektu (majiteli či řediteli firem) hodnocen jako jeden z nejpřínosnějších v tomto projektu. Návrh, model i vývoj systému byl zaměřen na obecné využití i v jiných oblastech (nejen průmyslových) pro zlepšování firemních procesů.352 - Katedra automatizační techniky a řízenívyhově

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Subset selection using nonlinear optimization

A common problem in computer science is how to represent a large dataset in a smaller more compact form. This thesis describes a generalized framework for selecting canonical subsets of data points that are highly representative of the original larger dataset. The contributions of the work are formulation of the subset selection problem as an optimization problem, an analysis of the complexity of the problem, the development of approximation algorithms to compute canonical subsets, and a demonstration of the utility of the algorithms in several problem domains.Ph.D., Computer Science -- Drexel University, 200

A framework for analyzing changes in health care lexicons and nomenclatures

Ontologies play a crucial role in current web-based biomedical applications for capturing contextual knowledge in the domain of life sciences. Many of the so-called bio-ontologies and controlled vocabularies are known to be seriously defective from both terminological and ontological perspectives, and do not sufficiently comply with the standards to be considered formai ontologies. Therefore, they are continuously evolving in order to fix the problems and provide valid knowledge. Moreover, many problems in ontology evolution often originate from incomplete knowledge about the given domain. As our knowledge improves, the related definitions in the ontologies will be altered. This problem is inadequately addressed by available tools and algorithms, mostly due to the lack of suitable knowledge representation formalisms to deal with temporal abstract notations, and the overreliance on human factors. Also most of the current approaches have been focused on changes within the internal structure of ontologies, and interactions with other existing ontologies have been widely neglected. In this research, alter revealing and classifying some of the common alterations in a number of popular biomedical ontologies, we present a novel agent-based framework, RLR (Represent, Legitimate, and Reproduce), to semi-automatically manage the evolution of bio-ontologies, with emphasis on the FungalWeb Ontology, with minimal human intervention. RLR assists and guides ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. Category theory has been used as a mathematical vehicle for modeling changes in ontologies and representing agents' interactions, independent of any specific choice of ontology language or particular implementation. We have also employed rule-based hierarchical graph transformation techniques to propose a more specific semantics for analyzing ontological changes and transformations between different versions of an ontology, as well as tracking the effects of a change in different levels of abstractions. Thus, the RLR framework enables one to manage changes in ontologies, not as standalone artifacts in isolation, but in contact with other ontologies in an openly distributed semantic web environment. The emphasis upon the generality and abstractness makes RLR more feasible in the multi-disciplinary domain of biomedical Ontology change management