MapReduce for parallel trace validation of LTL properties

Hallé, Stéphane; Soucy-Boivin, Maxime

doi:10.1186/s13677-015-0032-x

Cited by 5 publications

(6 citation statements)

References 22 publications

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“…Horizontal parallelization as in Barre et al [42] and Hallé and Soucy-Boivin [187] does not dependent on the actual events but is limited by the formula's structure. Vertical parallelization as in Basin et al [56] or parametric trace slicing [101,283] offers an a priori unbounded amount of parallelization but may lead to data duplication for certain formulas.…”

Section: Challengesmentioning

confidence: 89%

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A survey of challenges for runtime verification from advanced application domains (beyond software)

Sánchez

Schneider

Ahrendt

et al. 2019

Form Methods Syst Des

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Runtime verification is an area of formal methods that studies the dynamic analysis of execution traces against formal specifications. Typically, the two main activities in runtime verification efforts are the process of creating monitors from specifications, and the algorithms for the evaluation of traces against the generated monitors. Other activities involve the instrumentation of the system to generate the trace and the communication between the system under analysis and the monitor.Most of the applications in runtime verification have been focused on the dynamic analysis of software, even though there are many more potential applications to other computational devices and target systems. In this paper we present a collection of challenges for runtime verification extracted from concrete application domains, focusing on the difficulties that must be overcome to tackle these specific challenges. The computational models that characterize these domains require to devise new techniques beyond the current state of the art in runtime verification.

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Section: Challengesmentioning

confidence: 89%

“…Barre et al [42] and Hallé and Soucy-Boivin [187] use Hadoop's MapReduce framework to scale up the monitoring of propositional LTL properties using parallelization.…”

Section: Huge Data and Approximate Monitoringmentioning

confidence: 99%

A survey of challenges for runtime verification from advanced application domains (beyond software)

Sánchez

Schneider

Ahrendt

et al. 2019

Form Methods Syst Des

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show abstract

“…Closer to our approach is an algorithm introduced by Hallé et al that uses a cloud computing framework called MapReduce [27]. The algorithm can process multiple, arbitrary fragments of the trace in parallel, and compute its final result through a cycle of runs of MapReduce instances.…”

Section: Related Workmentioning

confidence: 99%

“…This discards the fact that offline monitoring generally has complete random access to the contents of the trace, a fact that could be leveraged to develop more efficient algorithms than for the online case. Moreover, because of the sequential nature of stateful properties, parallelizing their evaluation is a delicate operation that has produced mixed results so far [5,27].…”

Section: Introductionmentioning

confidence: 99%

Efficient Offline Monitoring of Linear Temporal Logic with Bit Vectors

Xie¹,

Hallé²

2020

Preprint

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A bitmap is a data structure designed to compactly represent sets of integers; it provides very fast operations for querying and manipulating such sets, exploiting bit-level parallelism. In this paper, we describe a technique for the offline verification of arbitrary expressions of Linear Temporal Logic using bitmap manipulation. An event trace is first preprocessed and transformed into a set of bitmaps. The LTL expression is then evaluated through a recursive procedure manipulating these bitmaps. Experimental results show that, for complex LTL formulae containing almost 20 operators, event traces can be evaluated at a throughput of millions of events per second.

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“…In an offline context, the leveraging of a cluster infrastructure was first suggested by Hallé et al, who introduced an algorithm for the automated verification of Linear Temporal Logic formulae on event traces, using an increasingly popular cloud computing framework called MapReduce [19]. The algorithm can process multiple, arbitrary fragments of the trace in parallel, and compute its final result through a cycle of runs of MapReduce instances.…”

Section: First-hand Parallelismmentioning

confidence: 99%

Event Stream Processing with Multiple Threads

Hallé

Khoury

Gaboury

2017

Runtime Verification

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Current runtime verification tools seldom make use of multi-threading to speed up the evaluation of a property on a large event trace. In this paper, we present an extension to the BeepBeep 3 event stream engine that allows the use of multiple threads during the evaluation of a query. Various parallelization strategies are presented and described on simple examples. The implementation of these strategies is then evaluated empirically on a sample of problems. Compared to the previous, single-threaded version of the BeepBeep engine, the allocation of just a few threads to specific portions of a query provides dramatic improvement in terms of running time.This paper is an extended version of the paper of same title published at the 2017 International Conference on Runtime Verification.

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MapReduce for parallel trace validation of LTL properties

Cited by 5 publications

References 22 publications

A survey of challenges for runtime verification from advanced application domains (beyond software)

A survey of challenges for runtime verification from advanced application domains (beyond software)

Efficient Offline Monitoring of Linear Temporal Logic with Bit Vectors

Event Stream Processing with Multiple Threads

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