I provide an overview of the Fault Tree Analysis method.I review different extensions of fault trees.A number of model-based dependability analysis approaches are reviewed.I outline the future outlook for model-based dependability analysis. Fault Tree Analysis (FTA) is a well-established and well-understood technique, widely used for dependability evaluation of a wide range of systems. Although many extensions of fault trees have been proposed, they suffer from a variety of shortcomings. In particular, even where software tool support exists, these analyses require a lot of manual effort. Over the past two decades, research has focused on simplifying dependability analysis by looking at how we can synthesise dependability information from system models automatically. This has led to the field of model-based dependability analysis (MBDA). Different tools and techniques have been developed as part of MBDA to automate the generation of dependability analysis artefacts such as fault trees. Firstly, this paper reviews the standard fault tree with its limitations. Secondly, different extensions of standard fault trees are reviewed. Thirdly, this paper reviews a number of prominent MBDA techniques where fault trees are used as a means for system dependability analysis and provides an insight into their working mechanism, applicability, strengths and challenges. Finally, the future outlook for MBDA is outlined, which includes the prospect of developing expert and intelligent systems for dependability analysis of complex open systems under the conditions of uncertainty.

https://hull-repository.worktribe.com/preview/582881/2018-02-04%2015186%20Kabir.pdf

An overview of fault tree analysis and its application in model based dependability analysis

https://www.cse.wustl.edu/~lu/papers/jsa14-virtualization.pdf

Challenges in real-time virtualization and predictable cloud computing

分析了基于处理器硬件虚拟化技术实现的KVM子系统的架构。针对KVM跟踪独立事件信息的局限性,提出一种新的KVM事件跟踪机制(kvmtrace)来达到性能调节的目的,并使用relayfs接口进行了设计与实现。同时探讨了Linux kernel Markers实现机制及其在kvmtrace的实际应用。

Kernel-based virtual machine事件跟踪机制的设计与实现

Network functions are increasingly being commoditized as software appliances on off-the-shelf machines, popularly known as Network Functions Virtualization (NFV). While this trend provides economics of scale, a key challenge is to ensure that the performance of virtual appliances match that of hardware boxes. We present the design and implementation of NFV-RT, a system that dynamically provisions resources in an NFV environment to provide timing guarantees. Specifically, given a set of service chains that each consist of some network functions, NFV-RT aims at maximizing the total number of requests that can be assigned to the cloud for each service chain, while ensuring that the assigned requests meet their deadlines. Our approach uses a linear programming model with randomized rounding to efficiently and proactively obtain a near-optimal solution. Our simulation shows that, given a cloud with thousands of machines and service chains, NFV-RT requires only a few seconds to compute the solution, while accepting three times the requests compared to baseline heuristics. In addition, under some special settings, NFV-RT can provide significant performance improvement. Our evaluation on a local testbed shows that 94% of the packets of the submitted requests meet their deadlines, which is three times that of previous reactive-based solutions.

Network functions virtualization with soft real-time guarantees

Robotic vehicles (RVs), such as drones and ground rovers, are a type of cyber-physical systems that operate in the physical world under the control of computing components in the cyber world. Despite RVs' robustness against natural disturbances, cyber or physical attacks against RVs may lead to physical malfunction and subsequently disruption or failure of the vehicles' missions. To avoid or mitigate such consequences, it is essential to develop attack detection techniques for RVs. In this paper, we present a novel attack detection framework to identify external, physical attacks against RVs on the fly by deriving and monitoring Control Invariants (CI). More specifically, we propose a method to extract such invariants by jointly modeling a vehicle's physical properties, its control algorithm and the laws of physics. These invariants are represented in a state-space form, which can then be implemented and inserted into the vehicle's control program binary for runtime invariant check. We apply our CI framework to eleven RVs, including quadrotor, hexarotor, and ground rover, and show that the invariant check can detect three common types of physical attacks -- including sensor attack, actuation signal attack, and parameter attack -- with very low runtime overhead.

https://dl.acm.org/doi/pdf/10.1145/3243734.3243752

Detecting Attacks Against Robotic Vehicles: A Control Invariant Approach

Recent years have witnessed two major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multicore processors are increasingly being used in real-time systems. The integration of real-time systems as virtual machines (VMs) atop common multicore platforms raises significant new research challenges in meeting the real-time performance requirements of multiple systems. This paper advances the state of the art in real-time virtualization by designing and implementing RT-Xen 2.0, a new real-time multicore VM scheduling framework in the popular Xen virtual machine monitor (VMM). RT-Xen 2.0 realizes a suite of real-time VM scheduling policies spanning the design space. We implement both global and partitioned VM schedulers; each scheduler can be configured to support dynamic or static priorities and to run VMs as periodic or deferrable servers. We present a comprehensive experimental evaluation that provides important insights into real-time scheduling on virtualized multicore platforms: (1) both global and partitioned VM scheduling can be implemented in the VMM at moderate overhead; (2) at the VMM level, while compositional scheduling theory shows partitioned EDF (pEDF) is better than global EDF (gEDF) in providing schedulability guarantees, in our experiments their performance is reversed in terms of the fraction of workloads that meet their deadlines on virtualized multi-core platforms; (3) at the guest OS level, pEDF requests a smaller total VCPU bandwidth than gEDF based on compositional scheduling analysis, and therefore using pEDF at the guest OS level leads to more schedulable workloads in our experiments; (4) a combination of pEDF in the guest OS and gEDF in the VMM -- configured with deferrable server -- leads to the highest fraction of schedulable task sets compared to other real-time VM scheduling policies; and (5) on a platform with a shared last-level cache, the benefits of global scheduling outweigh the cache penalty incurred by VM migration.

/pdf/real-time-multi-core-virtual-machine-scheduling-in-xen-4rxombe407.pdf

Real-time multi-core virtual machine scheduling in xen

This paper presents vCAT, a novel design for dynamic shared cache management on multicore virtualization platforms based on Intel's Cache Allocation Technology (CAT). Our design achieves strong isolation at both task and VM levels through cache partition virtualization, which works in a similar way as memory virtualization, but has challenges that are unique to cache and CAT. To demonstrate the feasibility and benefits of our design, we provide a prototype implementation of vCAT, and we present an extensive set of microbenchmarks and performance evaluation results on the PARSEC benchmarks and synthetic workloads, for both static and dynamic allocations. The evaluation results show that (i) vCAT can be implemented with minimal overhead, (ii) it can be used to mitigate shared cache interference, which could have caused task WCET increased by up to 7.2×, (iii) static management in vCAT can increase system utilization by up to 7× compared to a system without cache management, and (iv) dynamic management substantially outperforms static management in terms of schedulable utilization (increase by up to 3× in our multi-mode example use case).

/pdf/vcat-dynamic-cache-management-using-cat-virtualization-3et6c21n3q.pdf

vCAT: Dynamic Cache Management Using CAT Virtualization

Clouds have become appealing platforms for not only general-purpose applications, but also real-time ones. However, current clouds cannot provide real-time performance to virtual machines (VMs). We observe the demand and the advantage of co-hosting real-time (RT) VMs with non-real-time (regular) VMs in a same cloud. RT VMs can benefit from the easily deployed, elastic resource provisioning provided by the cloud, while regular VMs effectively utilize remaining resources without affecting the performance of RT VMs through proper resource management at both the cloud and the hyper visor levels. This paper presents RT-Open Stack, a cloud CPU resource management system for co-hosting real-time and regular VMs. RT-Open Stack entails three main contributions: (1) integration of a real-time hyper visor (RT-Xen) and a cloud management system (Open Stack) through a real-time resource interface, (2) a real-time VM scheduler to allow regular VMs to share hosts with RT VMs without interfering the real-time performance of RT VMs, and (3) a VM-to-host mapping strategy that provisions real-time performance to RT VMs while allowing effective resource sharing with regular VMs. Experimental results demonstrate that RT-Open Stack can effectively improve the real-time performance of RT VMs while allowing regular VMs to fully utilize the remaining CPU resources.

/pdf/rt-open-stack-cpu-resource-management-for-real-time-cloud-1oa1nqusga.pdf

RT-Open Stack: CPU Resource Management for Real-Time Cloud Computing

Multicore processors are becoming ubiquitous, and it is becoming increasingly common to run multiple real-time systems on a shared multicore platform. While this trend helps to reduce cost and to increase performance, it also makes it more challenging to achieve timing guarantees and functional isolation. One approach to achieving functional isolation is to use virtualization. However, virtualization also introduces many challenges to the multicore timing analysis, for instance, the overhead due to cache misses becomes harder to predict, since it depends not only on the direct interference between tasks but also on the indirect interference between virtual processors and the tasks executing on them. In this paper, we present a cache-aware compositional analysis technique that can be used to ensure timing guarantees of components scheduled on a multicore virtualization platform. Our technique improves on previous multicore compositional analyses by accounting for the cache-related overhead in the components' interfaces, and it addresses the new virtualization-specific challenges in the overhead analysis. To demonstrate the utility of our technique, we report results from an extensive evaluation based on randomly generated workloads.

/pdf/cache-aware-compositional-analysis-of-real-time-multicore-1rpwn96kgt.pdf

Cache-Aware Compositional Analysis of Real-Time Multicore Virtualization Platforms

We introduce gFPca, a cache-aware global pre-emptive fixed-priority (FP) scheduling algorithm with dynamic cache allocation for multicore systems, and we present its analysis and implementation. We introduce a new overhead-aware analysis that integrates several novel ideas to safely and tightly account for the cache overhead. Our evaluation shows that the proposed overhead-accounting approach is highly accurate, and that gFPca improves the schedulability of cache-intensive tasksets substantially compared to the cache-agnostic global FP algorithm. Our evaluation also shows that gFPca outperforms the existing cache-aware non- preemptive global FP algorithm in most cases. Through our implementation and empirical evaluation, we demonstrate the feasibility of cache-aware global scheduling with dynamic cache allocation and highlight scenarios in which gFPca is especially useful in practice.

/pdf/analysis-and-implementation-of-global-preemptive-fixed-n2jsqqbrwi.pdf

Meng Xu

Papers

Real-time multi-core virtual machine scheduling in xen

vCAT: Dynamic Cache Management Using CAT Virtualization

RT-Open Stack: CPU Resource Management for Real-Time Cloud Computing

Cache-Aware Compositional Analysis of Real-Time Multicore Virtualization Platforms

Analysis and Implementation of Global Preemptive Fixed-Priority Scheduling with Dynamic Cache Allocation