Fault Injection based Failure Analysis of three CentOS-like Operating Systems

The reliability of operating system (OS) has always been a major concern in the academia and industry. This paper studies how to perform OS failure analysis by fault injection based on the fault mode library. Firstly, we use the fault mode generation method based on Linux abstract hierarchy structure analysis to systematically define the Linux-like fault modes, construct a Linux fault mode library and develop a fault injection tool based on the fault mode library (FIFML). Then, fault injection experiments are carried out on three commercial Linux distributions, CentOS, Anolis OS and openEuler, to identify their reliability problems and give improvement suggestions. We also use the virtual file systems of these three OSs as experimental objects, to perform fault injection at levels of Light and Normal, measure the performance of 13 common file operations before and after fault injection.Comment: 9 pages, 8 figure

DETERMINING THE INFLUENCE OF THE NETWORK TIME PROTOCOL (NTP) ON THE DOMAIN NAME SERVICE SECURITY EXTENSION (DNSSEC) PROTOCOL

Recent hacking events against Sony Entertainment, Target, Home Depot, and bank Automated Teller Machines (ATMs) fosters a growing perception that the Internet is an insecure environment. While Internet Privacy Concerns (IPCs) continue to grow out of a general concern for personal privacy, the availability of inexpensive Internet-capable mobile devices increases the Internet of Things (IoT), a network of everyday items embedded with the ability to connect and exchange data. Domain Name Services (DNS) has been integral part of the Internet for name resolution since the beginning. Domain Name Services has several documented vulnerabilities; for example, cache poisoning. The solution adopted by the Internet Engineering Task Force (IETF) to strengthen DNS is DNS Security Extensions (DNSSEC). DNS Security Extensions uses support for cryptographically signed name resolution responses. The cryptography used by DNSSEC is the Public Key Infrastructure (PKI). Some researchers have suggested that the time stamp used in the public certificate of the name resolution response influences DNSSEC vulnerability to a Man-in-the-Middle (MiTM) attack. This quantitative study determined the efficacy of using the default relative Unix epoch time stamp versus an absolute time stamp provided by the Network Time Protocol (NTP). Both a two-proportion test and Fisher’s exact test were used on a large sample size to show that there is a statistically significant better performance in security behavior when using NTP absolute time instead of the traditional relative Unix epoch time with DNSSEC

Applying Mutable Object Snapshots to a High-level Object-Oriented Language

Software Engineers are familiar with mutable and immutable object state. Mutable objects shared across modules may lead to unexpected results as changes to the object in one module are visible to other modules sharing the object. When provided a mutable object as input in Java, it is common practice to defensively create a new private copy of the object bearing the same state via cloning, serializing/de-serializing, specialized object constructor, or third-party library. No universal approach exists for all scenarios and each common solution has well-known problems. This research explores the applicability of concepts within the Computer Engineering storage field related to snapshots. This exploration results in a simplified method of memory snapshotting implemented within OpenJDK 10. A novel runtime-managed method is proposed for declaring intent for object state to be unshared within the method signature. Preliminary experiments evaluate the attributes of this approach. A path for future research is proposed, including differential snapshots, alternative block sizes, improving performance, and exploring a tree of snapshots as a foundation to reason about changes to object state over time

An extensive study on iterative solver resilience : characterization, detection and prediction

Soft errors caused by transient bit flips have the potential to significantly impactan applicalion's behavior. This has motivated the design of an array of techniques to detect, isolate, and correct soft errors using microarchitectural, architectural, compilation­based, or application-level techniques to minimize their impact on the executing application. The first step toward the design of good error detection/correction techniques involves an understanding of an application's vulnerability to soft errors. This work focuses on silent data e orruption's effects on iterative solvers and efforts to mitigate those effects. In this thesis, we first present the first comprehensive characterizalion of !he impact of soft errors on !he convergen ce characteris tics of six iterative methods using application-level fault injection. We analyze the impact of soft errors In terms of the type of error (single-vs multi-bit), the distribution and location of bits affected, the data structure and statement impacted, and varialion with time. We create a public access database with more than 1.5 million fault injection results. We then analyze the performance of soft error detection mechanisms and present the comparalive results. Molivated by our observations, we evaluate a machine-learning based detector that takes as features that are the runtime features observed by the individual detectors to arrive al their conclusions. Our evalualion demonstrates improved results over individual detectors. We then propase amachine learning based method to predict a program's error behavior to make fault injection studies more efficient. We demonstrate this method on asse ssing the performance of soft error detectors. We show that our method maintains 84% accuracy on average with up to 53% less cost. We also show, once a model is trained further fault injection tests would cost 10% of the expected full fault injection runs.“Soft errors” causados por cambios de estado transitorios en bits, tienen el potencial de impactar significativamente el comportamiento de una aplicación. Esto, ha motivado el diseño de una variedad de técnicas para detectar, aislar y corregir soft errors aplicadas a micro-arquitecturas, arquitecturas, tiempo de compilación y a nivel de aplicación para minimizar su impacto en la ejecución de una aplicación. El primer paso para diseñar una buna técnica de detección/corrección de errores, implica el conocimiento de las vulnerabilidades de la aplicación ante posibles soft errors. Este trabajo se centra en los efectos de la corrupción silenciosa de datos en soluciones iterativas, así como en los esfuerzos para mitigar esos efectos. En esta tesis, primeramente, presentamos la primera caracterización extensiva del impacto de soft errors sobre las características convergentes de seis métodos iterativos usando inyección de fallos a nivel de aplicación. Analizamos el impacto de los soft errors en términos del tipo de error (único vs múltiples-bits), de la distribución y posición de los bits afectados, las estructuras de datos, instrucciones afectadas y de las variaciones en el tiempo. Creamos una base de datos pública con más de 1.5 millones de resultados de inyección de fallos. Después, analizamos el desempeño de mecanismos de detección de soft errors actuales y presentamos los resultados de su comparación. Motivados por las observaciones de los resultados presentados, evaluamos un detector de soft errors basado en técnicas de machine learning que toma como entrada las características observadas en el tiempo de ejecución individual de los detectores anteriores al llegar a su conclusión. La evaluación de los resultados obtenidos muestra una mejora por sobre los detectores individualmente. Basados en estos resultados propusimos un método basado en machine learning para predecir el comportamiento de los errores en un programa con el fin de hacer el estudio de inyección de errores mas eficiente. Presentamos este método para evaluar el rendimiento de los detectores de soft errors. Demostramos que nuestro método mantiene una precisión del 84% en promedio con hasta un 53% de mejora en el tiempo de ejecución. También mostramos que una vez que un modelo ha sido entrenado, las pruebas de inyección de errores siguientes costarían 10% del tiempo esperado de ejecución.Postprint (published version

Dynamical Systems

Complex systems are pervasive in many areas of science integrated in our daily lives. Examples include financial markets, highway transportation networks, telecommunication networks, world and country economies, social networks, immunological systems, living organisms, computational systems and electrical and mechanical structures. Complex systems are often composed of a large number of interconnected and interacting entities, exhibiting much richer global scale dynamics than the properties and behavior of individual entities. Complex systems are studied in many areas of natural sciences, social sciences, engineering and mathematical sciences. This special issue therefore intends to contribute towards the dissemination of the multifaceted concepts in accepted use by the scientific community. We hope readers enjoy this pertinent selection of papers which represents relevant examples of the state of the art in present day research. [...

Journal of Telecommunications and Information Technology, 2014, nr 2

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