Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors

Shlomi Dolev; Chryssis Georgiou; Ioannis Marcoullis; Elad M. Schiller

doi:10.1007/978-3-319-94147-9_7

Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors

Shlomi Dolev
, Chryssis Georgiou
, Ioannis Marcoullis
, Elad M. Schiller

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

10 Scopus citations

Abstract

Byzantine Fault Tolerant (BFT) replication leverages highly available cloud services and can facilitate the implementation of distributed ledgers, e.g., the blockchain. Systems providing BFT State Machine Replication (SMR) work under severe system assumptions, for example, that less than a third of replicas may suffer a Byzantine failure. Infrequent arbitrary violations of such design assumptions, may lead the system to an unintended state, and render it unavailable thereafter, requiring human intervention. Self-stabilization is a highly desirable system property that can complement Byzantine fault tolerant systems, and allow them to both tolerate Byzantine-failures and automatically recovery from any unintended state that assumption violations may lead to. This paper contributes the first self-stabilizing State Machine Replication service that is based on failure detectors. We suggest an implementable self-stabilizing failure detector to monitor both responsiveness and the replication progress. We thus encapsulate weaker synchronization guarantees than the previous self-stabilizing BFT SMR solution. We follow the seminal paper by Castro and Liskov of Practical Byzantine Fault Tolerance and focus on the self-stabilizing perspective. This work can aid towards building distributed blockchain system infrastructure enhanced with the self-stabilization design criteria.

Original language	English
Title of host publication	Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings
Editors	Itai Dinur, Shlomi Dolev, Sachin Lodha
Publisher	Springer Verlag
Pages	84-100
Number of pages	17
ISBN (Print)	9783319941462
DOIs	https://doi.org/10.1007/978-3-319-94147-9_7
State	Published - 1 Jan 2018
Event	2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018 - Beer-Sheva, Israel Duration: 21 Jun 2018 → 22 Jun 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10879 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018
Country/Territory	Israel
City	Beer-Sheva
Period	21/06/18 → 22/06/18

Keywords

Byzantine Fault-Tolerance
Fault detection
Self-stabilization
State Machine Replication

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-319-94147-9_7

Cite this

Dolev, S., Georgiou, C., Marcoullis, I., & Schiller, E. M. (2018). Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors. In I. Dinur, S. Dolev, & S. Lodha (Eds.), Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings (pp. 84-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10879 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-94147-9_7

Dolev, Shlomi ; Georgiou, Chryssis ; Marcoullis, Ioannis et al. / Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors. Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. editor / Itai Dinur ; Shlomi Dolev ; Sachin Lodha. Springer Verlag, 2018. pp. 84-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Byzantine Fault Tolerant (BFT) replication leverages highly available cloud services and can facilitate the implementation of distributed ledgers, e.g., the blockchain. Systems providing BFT State Machine Replication (SMR) work under severe system assumptions, for example, that less than a third of replicas may suffer a Byzantine failure. Infrequent arbitrary violations of such design assumptions, may lead the system to an unintended state, and render it unavailable thereafter, requiring human intervention. Self-stabilization is a highly desirable system property that can complement Byzantine fault tolerant systems, and allow them to both tolerate Byzantine-failures and automatically recovery from any unintended state that assumption violations may lead to. This paper contributes the first self-stabilizing State Machine Replication service that is based on failure detectors. We suggest an implementable self-stabilizing failure detector to monitor both responsiveness and the replication progress. We thus encapsulate weaker synchronization guarantees than the previous self-stabilizing BFT SMR solution. We follow the seminal paper by Castro and Liskov of Practical Byzantine Fault Tolerance and focus on the self-stabilizing perspective. This work can aid towards building distributed blockchain system infrastructure enhanced with the self-stabilization design criteria.",

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Dolev, S, Georgiou, C, Marcoullis, I & Schiller, EM 2018, Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors. in I Dinur, S Dolev & S Lodha (eds), Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10879 LNCS, Springer Verlag, pp. 84-100, 2nd International Symposium on Cyber Security Cryptography and Machine Learning, CSCML 2018, Beer-Sheva, Israel, 21/06/18. https://doi.org/10.1007/978-3-319-94147-9_7

Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors. / Dolev, Shlomi; Georgiou, Chryssis; Marcoullis, Ioannis et al.
Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. ed. / Itai Dinur; Shlomi Dolev; Sachin Lodha. Springer Verlag, 2018. p. 84-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10879 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Georgiou, Chryssis

AU - Marcoullis, Ioannis

AU - Schiller, Elad M.

PY - 2018/1/1

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N2 - Byzantine Fault Tolerant (BFT) replication leverages highly available cloud services and can facilitate the implementation of distributed ledgers, e.g., the blockchain. Systems providing BFT State Machine Replication (SMR) work under severe system assumptions, for example, that less than a third of replicas may suffer a Byzantine failure. Infrequent arbitrary violations of such design assumptions, may lead the system to an unintended state, and render it unavailable thereafter, requiring human intervention. Self-stabilization is a highly desirable system property that can complement Byzantine fault tolerant systems, and allow them to both tolerate Byzantine-failures and automatically recovery from any unintended state that assumption violations may lead to. This paper contributes the first self-stabilizing State Machine Replication service that is based on failure detectors. We suggest an implementable self-stabilizing failure detector to monitor both responsiveness and the replication progress. We thus encapsulate weaker synchronization guarantees than the previous self-stabilizing BFT SMR solution. We follow the seminal paper by Castro and Liskov of Practical Byzantine Fault Tolerance and focus on the self-stabilizing perspective. This work can aid towards building distributed blockchain system infrastructure enhanced with the self-stabilization design criteria.

AB - Byzantine Fault Tolerant (BFT) replication leverages highly available cloud services and can facilitate the implementation of distributed ledgers, e.g., the blockchain. Systems providing BFT State Machine Replication (SMR) work under severe system assumptions, for example, that less than a third of replicas may suffer a Byzantine failure. Infrequent arbitrary violations of such design assumptions, may lead the system to an unintended state, and render it unavailable thereafter, requiring human intervention. Self-stabilization is a highly desirable system property that can complement Byzantine fault tolerant systems, and allow them to both tolerate Byzantine-failures and automatically recovery from any unintended state that assumption violations may lead to. This paper contributes the first self-stabilizing State Machine Replication service that is based on failure detectors. We suggest an implementable self-stabilizing failure detector to monitor both responsiveness and the replication progress. We thus encapsulate weaker synchronization guarantees than the previous self-stabilizing BFT SMR solution. We follow the seminal paper by Castro and Liskov of Practical Byzantine Fault Tolerance and focus on the self-stabilizing perspective. This work can aid towards building distributed blockchain system infrastructure enhanced with the self-stabilization design criteria.

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Dolev S, Georgiou C, Marcoullis I, Schiller EM. Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors. In Dinur I, Dolev S, Lodha S, editors, Cyber Security Cryptography and Machine Learning - Second International Symposium, CSCML 2018, Proceedings. Springer Verlag. 2018. p. 84-100. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-94147-9_7

Self-stabilizing Byzantine Tolerant Replicated State Machine Based on Failure Detectors

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Keywords

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