Optimally simulating crash failures in a byzantine environment

K-Detector: Identifying Duplicate Crash Failures in Large-Scale Software Delivery

2020 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW) ◽

10.1109/issrew51248.2020.00028 ◽

2020 ◽

Author(s):

Hao Yang ◽

Yang Xu ◽

Yong Li ◽

Hyun-Deok Choi

Keyword(s):

Large Scale ◽

Crash Failures

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Message-optimal protocols for fault-tolerant broadcasts/multicasts in distributed systems with crash failures

IEEE Transactions on Computers ◽

10.1109/12.364545 ◽

1995 ◽

Vol 44 (2) ◽

pp. 346-352 ◽

Cited By ~ 3

Author(s):

Hong-Yi Tzeng ◽

Kai-Yeung Siu

Keyword(s):

Distributed Systems ◽

Fault Tolerant ◽

Crash Failures

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Cynthia Dwork and Yoram Moses. Knowledge and common knowledge in a Byzantine environment I: crash failures. Theoretical aspects of reasoning about knowledge, Proceedings of the 1986 conference, edited by Joseph Y. Halpern, Morgan Kaufmann Publishers, Los Altos1986, pp. 149–169.

Journal of Symbolic Logic ◽

10.1017/s0022481200028723 ◽

1988 ◽

Vol 53 (2) ◽

pp. 666-666

Author(s):

William J. Rapaport

Keyword(s):

Common Knowledge ◽

Crash Failures ◽

Reasoning About Knowledge

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An algorithm for tolerating crash failures in distributed systems

Proceedings Seventh IEEE International Conference and Workshop on the Engineering of Computer Based Systems (ECBS 2000) ◽

10.1109/ecbs.2000.839856 ◽

2002 ◽

Cited By ~ 7

Author(s):

V. De Florio ◽

G. Deconinck ◽

R. Lauwereins

Keyword(s):

Distributed Systems ◽

Crash Failures

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Time and Communication Efficient Consensus for Crash Failures

Lecture Notes in Computer Science - Distributed Computing ◽

10.1007/11864219_22 ◽

2006 ◽

pp. 314-328 ◽

Cited By ~ 11

Author(s):

Bogdan S. Chlebus ◽

Dariusz R. Kowalski

Keyword(s):

Crash Failures

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Stabilizing leader election in partial synchronous systems with crash failures

Journal of Parallel and Distributed Computing ◽

10.1016/j.jpdc.2009.09.004 ◽

2010 ◽

Vol 70 (1) ◽

pp. 45-58 ◽

Cited By ~ 2

Author(s):

C. Delporte-Gallet ◽

S. Devismes ◽

H. Fauconnier

Keyword(s):

Leader Election ◽

Synchronous Systems ◽

Crash Failures

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Revisiting simultaneous consensus with crash failures

Journal of Parallel and Distributed Computing ◽

10.1016/j.jpdc.2009.01.001 ◽

2009 ◽

Vol 69 (4) ◽

pp. 400-409 ◽

Cited By ~ 9

Author(s):

Yoram Moses ◽

Michel Raynal

Keyword(s):

Crash Failures

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Crash failures can drive protocols to arbitrary states

Proceedings of the fifteenth annual ACM symposium on Principles of distributed computing - PODC '96 ◽

10.1145/248052.248104 ◽

1996 ◽

Cited By ~ 12

Author(s):

Mahesh Jayaram ◽

George Varghese

Keyword(s):

Crash Failures

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A dynamic shadow approach for mobile agents to survive crash failures

Sixth IEEE International Symposium on Object-Oriented Real-Time Distributed Computing, 2003. ◽

10.1109/isorc.2003.1199243 ◽

2004 ◽

Cited By ~ 3

Author(s):

S. Pears ◽

Jie Xu ◽

C. Boldyreff

Keyword(s):

Mobile Agents ◽

Crash Failures

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Predicting Software Abnormal State by using Classification Algorithm

Research Anthology on Recent Trends, Tools, and Implications of Computer Programming ◽

10.4018/978-1-7998-3016-0.ch050 ◽

2021 ◽

pp. 1095-1113

Author(s):

Yongquan Yan ◽

Ping Guo

Keyword(s):

Classification Algorithm ◽

Software System ◽

Selection Algorithm ◽

Forward Selection ◽

Software Application ◽

Software Aging ◽

Crash Failures ◽

System Variables ◽

A Minor ◽

Fold Cross Validation

Software aging, also called smooth degradation or chronics, has been observed in a long running software application, accompanied by performance degradation, hang/crash failures or both. The key for software aging problem is how to fast and accurately detect software aging occurrence, which is a hard work due to the long delay before aging appearance. In this paper, two problems about software aging prediction are solved, which are how to accurately find proper running software system variables to represent system state and how to predict software aging state in a running software system with a minor error rate. Firstly, the authors use proposed stepwise forward selection algorithm and stepwise backward selection algorithm to find a proper subset of variables set. Secondly, a classification algorithm is used to model software aging process. Lastly, t-test with k-fold cross validation is used to compare performance of two classification algorithms. In the experiments, the authors find that their proposed method is an efficient way to forecast software aging problems in advance.

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