The Role of Reliability, Availability and Serviceability (RAS) Models in the Design and Evaluation of Self-Healing Systems

In an idealized scenario, self-healing systems predict, prevent or diagnose problems and take the appropriate actions to mitigate their impact with minimal human intervention. To determine how close we are to reaching this goal we require analytical techniques and practical approaches that allow us to quantify the effectiveness of a system's remediations mechanisms. In this paper we apply analytical techniques based on Reliability, Availability and Serviceability (RAS) models to evaluate individual remediation mechanisms of select system components and their combined effects on the system. We demonstrate the applicability of RAS-models to the evaluation of self-healing systems by using them to analyze various styles of remediations (reactive, preventative etc.), quantify the impact of imperfect remediations, identify sub-optimal (less effective) remediations and quantify the combined effects of all the activated remediations on the system as a whole

Simulation-based Fault Injection with QEMU for Speeding-up Dependability Analysis of Embedded Software

Simulation-based fault injection (SFI) represents a valuable solu- tion for early analysis of software dependability and fault tolerance properties before the physical prototype of the target platform is available. Some SFI approaches base the fault injection strategy on cycle-accurate models imple- mented by means of Hardware Description Languages (HDLs). However, cycle- accurate simulation has revealed to be too time-consuming when the objective is to emulate the effect of soft errors on complex microprocessors. To overcome this issue, SFI solutions based on virtual prototypes of the target platform has started to be proposed. However, current approaches still present some draw- backs, like, for example, they work only for specific CPU architectures, or they require code instrumentation, or they have a different target (i.e., design errors instead of dependability analysis). To address these disadvantages, this paper presents an efficient fault injection approach based on QEMU, one of the most efficient and popular instruction-accurate emulator for several microprocessor architectures. As main goal, the proposed approach represents a non intrusive technique for simulating hardware faults affecting CPU behaviours. Perma- nent and transient/intermittent hardware fault models have been abstracted without losing quality for software dependability analysis. The approach mini- mizes the impact of the fault injection procedure in the emulator performance by preserving the original dynamic binary translation mechanism of QEMU. Experimental results for both x86 and ARM processors proving the efficiency and effectiveness of the proposed approach are presented

Cyber Situational Awareness Using Live Hypervisor-Based Virtual Machine Introspection

In this research, a compiled memory analysis tool for virtualization (CMAT-V) is developed as a virtual machine introspection (VMI) utility to conduct live analysis during cyber attacks. CMAT-V leverages static memory dump analysis techniques to provide live dynamic system state data. Unlike some VMI applications, CMAT-V bridges the semantic gap using derivation techniques. CMAT-V detects Windows-based operating systems and uses the Microsoft Symbol Server to provide this context to the user. This research demonstrates the usefulness of CMAT-V as a situational awareness tool during cyber attacks, tests the detection of CMAT-V from the guest system level and measures its impact on host performance. During experimental testing, live system state information was successfully extracted from two simultaneously executing virtual machines (VM’s) under four rootkit-based malware attack scenarios. For each malware attack scenario, CMAT-V was able to provide evidence of the attack. Furthermore, data from CMAT-V detection testing did not confirm detection of the presence of CMAT-V’s live memory analysis from the VM itself. This supports the conclusion that CMAT-V does not create uniquely identifiable interference in the VM. Finally, three different benchmark tests reveal an 8% to 12% decrease in the host VM performance while CMAT-V is executing

Doctor of Philosophy

dissertationA modern software system is a composition of parts that are themselves highly complex: operating systems, middleware, libraries, servers, and so on. In principle, compositionality of interfaces means that we can understand any given module independently of the internal workings of other parts. In practice, however, abstractions are leaky, and with every generation, modern software systems grow in complexity. Traditional ways of understanding failures, explaining anomalous executions, and analyzing performance are reaching their limits in the face of emergent behavior, unrepeatability, cross-component execution, software aging, and adversarial changes to the system at run time. Deterministic systems analysis has a potential to change the way we analyze and debug software systems. Recorded once, the execution of the system becomes an independent artifact, which can be analyzed offline. The availability of the complete system state, the guaranteed behavior of re-execution, and the absence of limitations on the run-time complexity of analysis collectively enable the deep, iterative, and automatic exploration of the dynamic properties of the system. This work creates a foundation for making deterministic replay a ubiquitous system analysis tool. It defines design and engineering principles for building fast and practical replay machines capable of capturing complete execution of the entire operating system with an overhead of several percents, on a realistic workload, and with minimal installation costs. To enable an intuitive interface of constructing replay analysis tools, this work implements a powerful virtual machine introspection layer that enables an analysis algorithm to be programmed against the state of the recorded system through familiar terms of source-level variable and type names. To support performance analysis, the replay engine provides a faithful performance model of the original execution during replay

Virtualization with Limited Hardware Support

<p>In recent years, as mobile devices started to become an essential part of everyday computing, virtualization on mobile devices has begun to emerge as a solution for supporting multiple profiles on the same device. However, virtualization on mobile and embedded systems, and to a greater extent, on systems with limited hardware support for virtualization, often face different hardware environment than x86 platforms.</p><p>First of all, these platforms were usually equipped with CPUs that did not have hardware virtualization support. We propose a transparent and portable CPU virtualization solution for all types of CPUs that have hardware breakpoint functionality. We use a combination of the hardware breakpoint support and guest kernel control flow graph analysis to trap and emulate sensitive instructions.</p><p>Second, the traditional way of implementing record and replay which is an important feature of virtualization, cannot be implemented the same way on CPUs without hardware branch counters. We propose a record and replay implementation without using hardware branch counters on paravirtualized guests. We inspect guest virtual machine internal states to carefully rearrange recorded instructions during replay to achieve the same end result without having to literally repeat the same stream of instructions.</p><p>Third, these platforms are often equipped with storage systems with distinct I/O characteristics. SD card, for example, is a prevalent storage media on smartphones. We discuss the mismatch of I/O characteristics between SD card write speed characteristics and guest virtual machine workload characteristics using VMware Mobile Virtualization Platform as an example. We then propose a solution to bridge the gap and achieve efficient guest I/O when storing guest virtual disk images on SD cards.</p><p>This dissertation shows that it is possible to efficiently virtualize and provide advanced virtualization functionality to a range of systems without relying on x86 and PC specific virtualization technologies.</p>Dissertatio

Resilience-Building Technologies: State of Knowledge -- ReSIST NoE Deliverable D12

This document is the first product of work package WP2, "Resilience-building and -scaling technologies", in the programme of jointly executed research (JER) of the ReSIST Network of Excellenc

SimuBoost: Scalable Parallelization of Functional System Simulation

Für das Sammeln detaillierter Laufzeitinformationen, wie Speicherzugriffsmustern, wird in der Betriebssystem- und Sicherheitsforschung häufig auf die funktionale Systemsimulation zurückgegriffen. Der Simulator führt dabei die zu untersuchende Arbeitslast in einer virtuellen Maschine (VM) aus, indem er schrittweise Instruktionen interpretiert oder derart übersetzt, sodass diese auf dem Zustand der VM arbeiten. Dieser Prozess ermöglicht es, eine umfangreiche Instrumentierung durchzuführen und so an Informationen zum Laufzeitverhalten zu gelangen, die auf einer physischen Maschine nicht zugänglich sind. Obwohl die funktionale Systemsimulation als mächtiges Werkzeug gilt, stellt die durch die Interpretation oder Übersetzung resultierende immense Ausführungsverlangsamung eine substanzielle Einschränkung des Verfahrens dar. Im Vergleich zu einer nativen Ausführung messen wir für QEMU eine 30-fache Verlangsamung, wobei die Aufzeichnung von Speicherzugriffen diesen Faktor verdoppelt. Mit Simulatoren, die umfangreichere Instrumentierungsmöglichkeiten mitbringen als QEMU, kann die Verlangsamung um eine Größenordnung höher ausfallen. Dies macht die funktionale Simulation für lang laufende, vernetzte oder interaktive Arbeitslasten uninteressant. Darüber hinaus erzeugt die Verlangsamung ein unrealistisches Zeitverhalten, sobald Aktivitäten außerhalb der VM (z. B. Ein-/Ausgabe) involviert sind. In dieser Arbeit stellen wir SimuBoost vor, eine Methode zur drastischen Beschleunigung funktionaler Systemsimulation. SimuBoost führt die zu untersuchende Arbeitslast zunächst in einer schnellen hardwaregestützten virtuellen Maschine aus. Dies ermöglicht volle Interaktivität mit Benutzern und Netzwerkgeräten. Während der Ausführung erstellt SimuBoost periodisch Abbilder der VM (engl. Checkpoints). Diese dienen als Ausgangspunkt für eine parallele Simulation, bei der jedes Intervall unabhängig simuliert und analysiert wird. Eine heterogene deterministische Wiederholung (engl. heterogeneous deterministic Replay) garantiert, dass in dieser Phase die vorherige hardwaregestützte Ausführung jedes Intervalls exakt reproduziert wird, einschließlich Interaktionen und realistischem Zeitverhalten. Unser Prototyp ist in der Lage, die Laufzeit einer funktionalen Systemsimulation deutlich zu reduzieren. Während mit herkömmlichen Verfahren für die Simulation des Bauprozesses eines modernen Linux über 5 Stunden benötigt werden, schließt SimuBoost die Simulation in nur 15 Minuten ab. Dies sind lediglich 16% mehr Zeit, als der Bau in einer schnellen hardwaregestützten VM in Anspruch nimmt. SimuBoost ist imstande, diese Geschwindigkeit auch bei voller Instrumentierung zur Aufzeichnung von Speicherzugriffen beizubehalten. Die vorliegende Arbeit ist das erste Projekt, welches das Konzept der Partitionierung und Parallelisierung der Ausführungszeit auf die interaktive Systemvirtualisierung in einer Weise anwendet, die eine sofortige parallele funktionale Simulation gestattet. Wir ergänzen die praktische Umsetzung mit einem mathematischen Modell zur formalen Beschreibung der Beschleunigungseigenschaften. Dies erlaubt es, für ein gegebenes Szenario die voraussichtliche parallele Simulationszeit zu prognostizieren und gibt eine Orientierung zur Wahl der optimalen Intervalllänge. Im Gegensatz zu bisherigen Arbeiten legt SimuBoost einen starken Fokus auf die Skalierbarkeit über die Grenzen eines einzelnen physischen Systems hinaus. Ein zentraler Schlüssel hierzu ist der Einsatz moderner Checkpointing-Technologien. Im Rahmen dieser Arbeit präsentieren wir zwei neuartige Methoden zur effizienten und effektiven Kompression von periodischen Systemabbildern