arXiv subjects

Sofiene Tahar

Publications and source records attributed to Sofiene Tahar.

25 records · Page 2Linked to original sources

arXiv2017

Dynamic Fault Trees Analysis using an Integration of Theorem Proving and Model Checking

Dynamic fault trees (DFTs) have emerged as an important tool for capturing the dynamic behavior of system failure. These DFTs are then analyzed qualitatively and quantitatively using stochastic or algebraic methods to judge the failure characteristics of the given system in terms of the failures of its sub-components. Model checking has been recently proposed to conduct the failure analysis of systems using DFTs with the motivation to provide a rigorous failure analysis of safety-critical systems. However, model checking has not been used for the DFT qualitative analysis and the reduction algorithms used in model checking are usually not formally verified. Moreover, the analysis time grows exponentially with the increase of the number of states. These issues limit the usefulness of model checking for analyzing complex systems used in safety-critical domains, where the accuracy and completeness of analysis matters the most. To overcome these limitations, we propose a comprehensive methodology to perform the qualitative and quantitative analysis of DFTs using an integration of theorem proving and model checking based approaches. For this purpose, we formalized all the basic dynamic fault tree gates using higher-order logic based on the algebraic approach and formally verified some of the simplification properties. This formalization allows us to formally verify the equivalence between the original and reduced DFTs using a theorem prover, and conduct the qualitative analysis. We then use model checking to perform the quantitative analysis of the formally verified reduced DFT. We applied our methodology to five benchmarks and the results show that the formally verified reduced DFT was analyzed using model checking with up to six times less states and up to 133000 times faster.

cs.LO↗

arXiv2016

Formal Dependability Modeling and Analysis: A Survey

Dependability is an umbrella concept that subsumes many key properties about a system, including reliability, maintainability, safety, availability, confidentiality, and integrity. Various dependability modeling techniques have been developed to effectively capture the failure characteristics of systems over time. Traditionally, dependability models are analyzed using paper-and-pencil proof methods and computer based simulation tools but their results cannot be trusted due to their inherent inaccuracy limitations. The recent developments in probabilistic analysis support using formal methods have enabled the possibility of accurate and rigorous dependability analysis. Thus, the usage of formal methods for dependability analysis is widely advocated for safety-critical domains, such as transportation, aerospace and health. Given the complementary strengths of mainstream formal methods, like theorem proving and model checking, and the variety of dependability models judging the most suitable formal technique for a given dependability model is not a straightforward task. In this paper, we present a comprehensive review of existing formal dependability analysis techniques along with their pros and cons for handling a particular dependability model.

cs.SE↗

arXiv2015

ATLAS: An Adaptive Failure-aware Scheduler for Hadoop

Hadoop has become the de facto standard for processing large data in today's cloud environment. The performance of Hadoop in the cloud has a direct impact on many important applications ranging from web analytic, web indexing, image and document processing to high-performance scientific computing. However, because of the scale, complexity and dynamic nature of the cloud, failures are common and these failures often impact the performance of jobs running in Hadoop. Although Hadoop possesses built-in failure detection and recovery mechanisms, several scheduled jobs still fail because of unforeseen events in the cloud environment. A single task failure can cause the failure of the whole job and unpredictable job running times. In this report, we propose ATLAS (AdapTive faiLure-Aware Scheduler), a new scheduler for Hadoop that can adapt its scheduling decisions to events occurring in the cloud environment. Using statistical models, ATLAS predicts task failures and adjusts its scheduling decisions on the fly to reduce task failure occurrences. We implement ATLAS in the Hadoop framework of Amazon Elastic MapReduce (EMR) and perform a case study to compare its performance with those of the FIFO, Fair and Capacity schedulers. Results show that ATLAS can reduce the percentage of failed jobs by up to 28% and the percentage of failed tasks by up to 39%, and the total execution time of jobs by 10 minutes on average. ATLAS also reduces CPU and memory usages.

cs.DC↗

arXiv2015

Predicting Scheduling Failures in the Cloud

Cloud Computing has emerged as a key technology to deliver and manage computing, platform, and software services over the Internet. Task scheduling algorithms play an important role in the efficiency of cloud computing services as they aim to reduce the turnaround time of tasks and improve resource utilization. Several task scheduling algorithms have been proposed in the literature for cloud computing systems, the majority relying on the computational complexity of tasks and the distribution of resources. However, several tasks scheduled following these algorithms still fail because of unforeseen changes in the cloud environments. In this paper, using tasks execution and resource utilization data extracted from the execution traces of real world applications at Google, we explore the possibility of predicting the scheduling outcome of a task using statistical models. If we can successfully predict tasks failures, we may be able to reduce the execution time of jobs by rescheduling failed tasks earlier (i.e., before their actual failing time). Our results show that statistical models can predict task failures with a precision up to 97.4%, and a recall up to 96.2%. We simulate the potential benefits of such predictions using the tool kit GloudSim and found that they can improve the number of finished tasks by up to 40%. We also perform a case study using the Hadoop framework of Amazon Elastic MapReduce (EMR) and the jobs of a gene expression correlations analysis study from breast cancer research. We find that when extending the scheduler of Hadoop with our predictive models, the percentage of failed jobs can be reduced by up to 45%, with an overhead of less than 5 minutes.

cs.DC↗

arXiv2014

Towards the Formal Reliability Analysis of Oil and Gas Pipelines

It is customary to assess the reliability of underground oil and gas pipelines in the presence of excessive loading and corrosion effects to ensure a leak-free transport of hazardous materials. The main idea behind this reliability analysis is to model the given pipeline system as a Reliability Block Diagram (RBD) of segments such that the reliability of an individual pipeline segment can be represented by a random variable. Traditionally, computer simulation is used to perform this reliability analysis but it provides approximate results and requires an enormous amount of CPU time for attaining reasonable estimates. Due to its approximate nature, simulation is not very suitable for analyzing safety-critical systems like oil and gas pipelines, where even minor analysis flaws may result in catastrophic consequences. As an accurate alternative, we propose to use a higher-order-logic theorem prover (HOL) for the reliability analysis of pipelines. As a first step towards this idea, this paper provides a higher-order-logic formalization of reliability and the series RBD using the HOL theorem prover. For illustration, we present the formal analysis of a simple pipeline that can be modeled as a series RBD of segments with exponentially distributed failure times.

cs.LO↗

arXiv2014

Formalization of Complex Vectors in Higher-Order Logic

Complex vector analysis is widely used to analyze continuous systems in many disciplines, including physics and engineering. In this paper, we present a higher-order-logic formalization of the complex vector space to facilitate conducting this analysis within the sound core of a theorem prover: HOL Light. Our definition of complex vector builds upon the definitions of complex numbers and real vectors. This extension allows us to extensively benefit from the already verified theorems based on complex analysis and real vector analysis. To show the practical usefulness of our library we adopt it to formalize electromagnetic fields and to prove the law of reflection for the planar waves.

cs.LO↗

arXiv2014

Formal Analysis of Optical Systems

Optical systems are becoming increasingly important by resolving many bottlenecks in today's communication, electronics, and biomedical systems. However, given the continuous nature of optics, the inability to efficiently analyze optical system models using traditional paper-and-pencil and computer simulation approaches sets limits especially in safety-critical applications. In order to overcome these limitations, we propose to employ higher-order-logic theorem proving as a complement to computational and numerical approaches to improve optical model analysis in a comprehensive framework. The proposed framework allows formal analysis of optical systems at four abstraction levels, i.e., ray, wave, electromagnetic, and quantum.

cs.LO↗