ProFIPy: Programmable Software Fault Injection as-a-Service

Cotroneo, Domenico; Simone, Luigi De; Liguori, Pietro; Natella, Roberto

doi:10.1109/dsn48063.2020.00052

Cited by 19 publications

(8 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We developed an automated tool to scan the source code of Nova, Cinder, and Neutron to find all the injectable API calls, and to introduce faults by mutating the calls [52]. For each workload, we identified the injectable locations that were covered by the workload itself, and we performed one fault injection test per covered location.…”

Section: Methodsmentioning

confidence: 99%

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Cotroneo,

De Simone,

Liguori

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Cloud computing systems fail in complex and unexpected ways due to unexpected combinations of events and interactions between hardware and software components. Fault injection is an effective means to bring out these failures in a controlled environment. However, fault injection experiments produce massive amounts of data, and manually analyzing these data is inefficient and error-prone, as the analyst can miss severe failure modes that are yet unknown. This paper introduces a new paradigm (fault injection analytics) that applies unsupervised machine learning on execution traces of the injected system, to ease the discovery and interpretation of failure modes. We evaluated the proposed approach in the context of fault injection experiments on the OpenStack cloud computing platform, where we show that the approach can accurately identify failure modes with a low computational cost.

show abstract

Section: Methodsmentioning

confidence: 99%

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Cotroneo,

De Simone,

Liguori

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The authors use a tool which generates an Abstract Syntax Tree and inject faults in the tree, converting the result back to source code and place their focus on understanding failures. The authors have recently further developed their work in Cotroneo et al (2020) to propose a fault injection tool offered as a service, which supports the user through the definition of some of the key steps of a fault injection campaign (e.g., workload and faultload definition, failure data analysis).…”

Section: Background and Related Workmentioning

confidence: 99%

Injecting software faults in Python applications

2021

View full text Add to dashboard Cite

Software fault injection techniques have been largely used as means for evaluating the dependability of systems in presence of certain types of faults. Despite the large diversity of tools offering the possibility of emulating the presence of software faults, there is little practical support for emulating the presence of software faults in Python applications, which are increasingly being used to support business critical cloud services. In this paper, we present FIT4Python, a tool for injecting software faults in Python code and then use it, in a mutation testing campaign, to analyse the effectiveness of OpenStack's test suite against new probable software faults. We begin by analysing the types of faults affecting Nova Compute, the core component of OpenStack. We use our tool to emulate the presence of new faults in Nova Compute API to understand how well OpenStack's battery of unit, functional, and integration tests cover these new, but probable, situations. Results show clear limitations in the effectiveness of OpenStack developers' test suites, with many cases of injected faults passing undetected through all three types of tests and that nearly half of the analysed problems could be detected with trivial changes or additions to the unit tests.

show abstract

“…A requirement we set for our experiments was that the software fault should only be activated while the workload (i.e., Solr) is being executed, thus after any preparatory steps (e.g., creating base save files) have been completed. This was reached by encompassing the software fault inside an if whose activation depends on a variable (a similar practice has been used in other works [53], [54]) that is manipulated by the microvisor using VMI. When the value of the variable is false, the non-faulty code is executed.…”

Section: Fault Injectionmentioning

confidence: 99%

Mitigating Virtualization Failures Through Migration to a Co-Located Hypervisor

2021

View full text Add to dashboard Cite

Many organizations are moving their systems to the cloud, where providers consolidate multiple clients using virtualization, which creates challenges to business-critical applications. Research has shown that hypervisors fail, often causing common-mode failures that may abruptly disrupt dozens of virtual machines simultaneously. We hypothesize and empirically show that a significant percentage of virtual machines affected by a hypervisor failure are capable of continuing execution on a new hypervisor. Supported by this observation, we design a technique for recovering from hypervisor failures through efficient virtual machine migration to a co-located hypervisor, which allows virtual machines to continue executing with minimal downtime and which can be transparently applied to existing applications. We evaluate a proof-of-concept implementation using fault injection of hardware and software faults and show that it can recover, on average, 41-46% of all virtual machines, as well as having a mean virtual machine downtime of 3 seconds.

show abstract

ProFIPy: Programmable Software Fault Injection as-a-Service

Cited by 19 publications

References 38 publications

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Fault Injection Analytics: A Novel Approach to Discover Failure Modes in Cloud-Computing Systems

Injecting software faults in Python applications

Mitigating Virtualization Failures Through Migration to a Co-Located Hypervisor

Contact Info

Product

Resources

About