Distributed Integrity Checking for Systems with Replicated Data

Ziwich, Roverli Pereira; Duarte, Elias P.; Albini, Luiz Carlos Pessoa

doi:10.1109/icpads.2005.130

Cited by 11 publications

(12 citation statements)

References 12 publications

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“…In Albini et al [2005] and Albini and Duarte Jr. [2001] a hierarchical comparisonbased adaptive distributed system-level diagnosis algorithm-called Hi-Comp-is presented based on the generalized model.…”

Section: Generalized Models For Distributed Comparison-based Diagnosismentioning

confidence: 98%

“…The Generalized Distributed Comparison-based (GDC) model [Albini et al 2005;Albini and Duarte Jr. 2001] assumes a fully connected system S also represented by a graph G = {V, E), in which V/ e V and Wj e V,3 {i, j) e E. This model incorporates all the assumptions of the MM model, plus one: the time for a fault-free node to produce an output for a task is bounded. This model is fully distributed, that is, fault-free nodes both execute the comparisons and diagnose the system based on the comparison syndrome.…”

Section: Generalized Models For Distributed Comparison-based Diagnosismentioning

confidence: 99%

“…Another general hierarchical comparison-based adaptive distributed diagnosis (GDC*) model was proposed in Ziwich et al [2005]. In this model a fault-free node tests other nodes and based on test results, classifies the tested nodes in sets.…”

Section: Generalized Models For Distributed Comparison-based Diagnosismentioning

confidence: 99%

“…All fault-free nodes execute all comparisons and diagnose the system. Other comparison-based models that are fully distributed but do not employ a reliable broadcast primitive were presented in Albini and Duarte Jr. [2001] and Ziwich et al [2005], in which fault-free nodes test and classify the system nodes in sets.…”

Section: Introductionmentioning

confidence: 99%

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A survey of comparison-based system-level diagnosis

2011

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ParanáThe growing complexity and dependability requirements of hardware, software, emd networks demand efficient techniques for discovering disruptive behavior in those systems. Comparison-based diagnosis is a realistic approach to detect faulty units based on the outputs of tasks executed by system units. This survey integrates the vast amount of research efforts that have been produced in this field, from the earliest theoretical models to new promising applications. Key results also include the quantitative evaluation of a relevant reliability metric-the diagnosability-of several popular interconnection network topologies. Relevant diagnosis algorithms are also described. The survey aims at clarifying and uncovering the potential of this technology, which can be applied to improve the dependability of diverse complex computer systems. . 2011. A survey of comparison-based system-level diagnosis.

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Section: Generalized Models For Distributed Comparison-based Diagnosismentioning

confidence: 98%

Section: Generalized Models For Distributed Comparison-based Diagnosismentioning

confidence: 99%

Section: Generalized Models For Distributed Comparison-based Diagnosismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A survey of comparison-based system-level diagnosis

2011

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“…In [31] a generalized comparison-based diagnosis model is presented in which the units themselves are able to perform the system diagnosis, instead of a central observer. And most important, one of the main differences of this model to others is that the comparison result of tasks executed by pairs of faulty units may result in a match.…”

Section: Comparison-based Diagnosismentioning

confidence: 99%

Diagnosis of Content Pollution in P2P Live Streaming Networks

Ziwich

Schimidt

Duarte

et al. 2013

2013 Sixth Latin-American Symposium on Dependable Computing

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Content pollution is one of the challenges for massively deploying live streaming P2P networks in the Internet. As the peers themselves are responsible to retransmit data, there is no trivial solution to this problem. This work presents a new strategy to detect content pollution that employs comparison-based diagnosis to identify modifications on the data stream. A peer compares randomly selected chunks received from its neighbors. Based on the comparison results, peers that transmitted polluted content are identified. The proposed solution was implemented using Fireflies, a scalable and intrusion-tolerant overlay network. Experimental results show that the strategy represents a feasible solution to detect content pollution and causes a low overhead in terms of network bandwidth.

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Scalable Uncertainty-Tolerant Business Rules

Cuzzocrea

Decker

Muñoz-Escoí

2014

Lecture Notes in Computer Science

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Abstract. Business rules are of key importance for maintaining the correctness of business processes and the reliability of business data. When they take the form of integrity constraints, business rules also can help to contain the amount of uncertainty associated to business data and decisions based on those data. However, business rule enforcement may not scale up easily to systems with concurrent transactions. To a large extent, the problem is due to two common exigencies: the postulates of total and of isolated business rule satisfaction. In order to limit the accumulation of business rule violations, and thus of uncertainty, we are going to outline how a measure-based uncertainty-tolerant approach to business rules maintenance scales up to concurrent transactions. The scale-up is achieved by refraining from the postulates of total and isolated business rule satisfaction.

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Distributed Integrity Checking for Systems with Replicated Data

Abstract: This work presents a new comparison-based diagnosis model and a new algorithm, called

Cited by 11 publications

References 12 publications

A survey of comparison-based system-level diagnosis

A survey of comparison-based system-level diagnosis

Diagnosis of Content Pollution in P2P Live Streaming Networks

Scalable Uncertainty-Tolerant Business Rules

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