FastFI: Accelerating Software Fault Injections

Schwahn, Oliver; Coppik, Nicolas; Winter, Stefan; Suri, Neeraj

doi:10.1109/prdc.2018.00035

Cited by 9 publications

(6 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other techniques rely on more lightweight process isolation to avoid the overhead of strong VM isolation. This includes AFEX [4] as well as FastFI [25]. FastFI accelerates SFI experiments through parallelization and by avoiding redundant executions of code that is common to multiple faults and of faulty versions in which the fault location is never reached under a given workload.…”

Section: A Sfi Test Latenciesmentioning

confidence: 99%

“…As our approach is conceptually related to that of FastFI [25], we first briefly summarize the FastFI execution model in Sec. III-C1 and discuss why it is not directly applicable to OS kernels.…”

Section: Improving Kernel Epa With Fast Vm Cloningmentioning

confidence: 99%

“…Here, F 1 through F 3 are only executed once, and new processes are forked for each faulty version when a function for which such versions exist is reached. While this approach can achieve substantial speedups as shown in [25], it is not applicable to kernel code for several reasons.…”

Section: Figmentioning

confidence: 99%

See 2 more Smart Citations

Fast Kernel Error Propagation Analysis in Virtualized Environments

Coppik

Schwahn

Suri

2021

2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST)

Self Cite

View full text Add to dashboard Cite

Assessing operating system dependability remains a challenging problem, particularly in monolithic systems. Component interfaces are not well-defined and boundaries are not enforced at runtime. This allows faults in individual components to arbitrarily affect other parts of the system. Software fault injection (SFI) can be used to experimentally assess the resilience of such systems in the presence of faulty components. However, applying SFI to complex, monolithic operating systems poses challenges due to long test latencies and the difficulty of detecting corruptions in the internal state of the operating system.In this paper, we present a novel approach that leverages static and dynamic analysis alongside modern operating system and virtual machine features to reduce SFI test latencies for operating system kernel components while enabling efficient and accurate detection of internal state corruptions.We demonstrate the feasibility of our approach by applying it to multiple widely used Linux file systems.

show abstract

Section: A Sfi Test Latenciesmentioning

confidence: 99%

Section: Improving Kernel Epa With Fast Vm Cloningmentioning

confidence: 99%

See 1 more Smart Citation

Fast Kernel Error Propagation Analysis in Virtualized Environments

Coppik

Schwahn

Suri

2021

2021 14th IEEE Conference on Software Testing, Verification and Validation (ICST)

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on this analysis, they defined a software fault model with 13 fault types, covering 60% of the sample of bugs in the open-source projects [14]. This fault model was used in several other tools, including SAFE [35], HSFI [36], and FastFI [37]. However, these tools focus on a fixed software fault model, with no ability to customize the injected faults according to the specific needs of a project or company.…”

Section: Related Workmentioning

confidence: 99%

ProFIPy: Programmable Software Fault Injection as-a-Service

Cotroneo,

De Simone,

Liguori

et al. 2020

Preprint

View full text Add to dashboard Cite

In this paper, we present a new fault injection tool (ProFIPy) for Python software. The tool is designed to be programmable, in order to enable users to specify their software fault model, using a domain-specific language (DSL) for fault injection. Moreover, to achieve better usability, ProFIPy is provided as software-as-a-service and supports the user through the configuration of the faultload and workload, failure data analysis, and full automation of the experiments using containerbased virtualization and parallelization.

show abstract

“…Unfortunately, fault injection has a high entry barrier, and it is still beyond the reach of the minor service providers, due to the cost and complexity of planning and orchestrate fault injection experiments. Research has been addressing this challenge by developing many solutions applicable both at compile- [15,16] and run-time [17,18]; domain-specific languages to tailor fault injection for the specific system [19,20,21,22]; heuristics aim at minimizing the number of fault injection experiments to identify weak points in the system [23,24,25]; and automated techniques for analyzing fault injection data [26,27]. Despite these advances, it is still difficult to deploy fault injection toolchains into cloud infrastructures.…”

Section: Introductionmentioning

confidence: 99%

ThorFI: A Novel Approach for Network Fault Injection as a Service

Cotroneo¹,

Simone²,

Natella³

2022

Preprint

View full text Add to dashboard Cite

In this work, we present a novel fault injection solution (ThorFI ) for virtual networks in cloud computing infrastructures. ThorFI is designed to provide non-intrusive fault injection capabilities for a cloud tenant, and to isolate injections from interfering with other tenants on the infrastructure. We present the solution in the context of the OpenStack cloud management platform, and release this implementation as open-source software. Finally, we present two relevant case studies of ThorFI, respectively in an NFV IMS and of a highavailability cloud application. The case studies show that ThorFI can enhance functional tests with fault injection, as in 4%-34% of the test cases the IMS is unable to handle faults; and that despite redundancy in virtual networks, faults in one virtual network segment can propagate to other segments, and can affect the throughput and response time of the cloud application as a whole, by about 3 times in the worst case.

show abstract

FastFI: Accelerating Software Fault Injections

Cited by 9 publications

References 24 publications

Fast Kernel Error Propagation Analysis in Virtualized Environments

Fast Kernel Error Propagation Analysis in Virtualized Environments

ProFIPy: Programmable Software Fault Injection as-a-Service

ThorFI: A Novel Approach for Network Fault Injection as a Service

Contact Info

Product

Resources

About