Fault Injection in Virtualized Systems—Challenges and Applications

Le, Michael; Tamir, Yuval

doi:10.1109/tdsc.2014.2334300

Cited by 15 publications

(8 citation statements)

References 22 publications

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“…Hu et al set up a system-level fault injection platform based on the Simics simulator, and studied the impact of hardware fault on a multi-core system through software simulation, including operating system and application program [31]. Le and Tamir proposed fault injection tools based on cloud environments, taking advantage of virtualization environment (virtual machine monitor) to implement a fault injection interface toward the upper layers [32]. As fault injection modules are (and can only be) implemented in a virtual machine monitor, only misbehaviors of the guest operating system fall into the observation scope and can be tracked.…”

Section: Fault Injectorsmentioning

confidence: 99%

End-to-end diagnosis of cloud systems against intermittent faults

Wang

Huo

2021

COMSIS J

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The diagnosis of intermittent faults is challenging because of their random manifestation due to intricate mechanisms. Conventional diagnosis methods are no longer effective for these faults, especially for hierachical environment, such as cloud computing. This paper proposes a fault diagnosis method that can effectively identify and locate intermittent faults originating from (but not limited to) processors in the cloud computing environment. The method is end-to-end in that it does not rely on artificial feature extraction for applied scenarios, making it more generalizable than conventional neural network-based methods. It can be implemented with no additional fault detection mechanisms, and is realized by software with almost zero hardware cost. The proposed method shows a higher fault diagnosis accuracy than BP network, reaching 97.98% with low latency.

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Section: Fault Injectorsmentioning

confidence: 99%

End-to-end diagnosis of cloud systems against intermittent faults

Wang

Huo

2021

COMSIS J

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“…Figure 5 shows this setup when the target system is the 3AppVM configuration. This setup simplified and sped up the fault injection campaigns by facilitating the restart of the target system and refresh its disk images after each injection run to isolate the effects of faults injected in different runs [16,22]. Since there is a potential that running the target system in a VM may bias the results, we have run experiments to validate the 3AppVM results on bare hardware (Section 9).…”

Section: System Configurationsmentioning

confidence: 99%

“…Furthermore, in the Use bad function arguments * When the target system is run inside a FV VM, we do not inject into the reserved bits and the VM-8086 bit of the EFL register. This is due to the limitation of performing fault injection into a VM as some hardware faults cannot be accurately emulated [22].…”

Section: Workloadsmentioning

confidence: 99%

“…The goal is to first expose problem areas and then evaluate the effectiveness of the refined mechanisms to recover from a variety of system corruptions. We used the UCLA Gigan fault injector [16,9,22] to inject faults into different VI components. Gigan can reside in the VMM and inject many types of faults into the VMs and the VMM.…”

Section: Fault Injection Campaigns and Fault Typesmentioning

confidence: 99%

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Resilient Virtualized Systems Using ReHype

Le,

Tamir

2021

Preprint

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System-level virtualization introduces critical vulnerabilities to failures of the software components that implement virtualization -the virtualization infrastructure (VI). To mitigate the impact of such failures, we introduce a resilient VI (RVI) that can recover individual VI components from failure, caused by hardware or software faults, transparently to the hosted virtual machines (VMs). Much of the focus is on the ReHype mechanism for recovery from hypervisor failures, that can lead to state corruption and to inconsistencies among the states of system components.ReHype's implementation for the Xen hypervisor was done incrementally, using fault injection results to identify sources of critical corruption and inconsistencies. This implementation involved 900 LOC, with memory space overhead of 2.1MB. Fault injection campaigns, with a variety of fault types, show that ReHype can successfully recover, in less than 750ms, from over 88% of detected hypervisor failures.In addition to ReHype, recovery mechanisms for the other VI components are described. The overall effectiveness of our RVI is evaluated hosting a Web service application, on a cluster of VMs. With faults in any VI component, for over 87% of detected failures, our recovery mechanisms allow services provided by the application to be continuously maintained despite the resulting failures of VI components.

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“…Some of them are able to emulate upsets in FPGA fabric and microprocessor cache lines and special registers. 42 In Le and Tamir 43 and Pham et al, 44 fault injection tools implemented in cloud environments are proposed. The authors use them to evaluate and characterize dependability of OSs, taking advantage of virtualization (Virtual Machine Monitor) for fault injection into nonvirtualized systems (GuestOS).…”

Section: Related Workmentioning

confidence: 99%

Quantitative evaluation of fault propagation in a commercial cloud system

Wang

2020

International Journal of Distributed Sensor Networks

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As semiconductor technology scales into the nano regime, hardware faults have been threats against computational devices. Cloud systems are incorporating more and more computing density and energy into themselves; thus, fundamental research on topics such as dependability validation is needed, in order to verify the robustness of clouds for sensor networks. However, dependability evaluation studies have often been carried out beyond isolated physical systems, such as processors, sensors, and single boards with or without operating system hosts. These studies have been performed using inaccurate simulations instead of validating complete cloud software stacks (firmware, hypervisor, operating system hosts and workloads) as a whole. In this article, we describe the implementation of a fault injection tool, which validates the dependability of a commercial cloud software stack. Hardware faults induced by high energy density environments can be injected; the fault propagation through the cloud software stack is traced, and quantitatively evaluated. Experimental results show that the integrated fault detection mechanism of the cloud system, such as fatal trap detectors, has left a detection margin of 20% silent data corruption to narrow down. We additionally propose two detection mechanisms, which proved good performance in fault detection of cloud systems.

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Fault Injection in Virtualized Systems—Challenges and Applications

Cited by 15 publications

References 22 publications

End-to-end diagnosis of cloud systems against intermittent faults

End-to-end diagnosis of cloud systems against intermittent faults

Resilient Virtualized Systems Using ReHype

Quantitative evaluation of fault propagation in a commercial cloud system

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