DocumentCode :
782738
Title :
Modeling of correlated failures and community error recovery in multiversion software
Author :
Nicola, Victor F. ; Goyal, Ambuj
Author_Institution :
IBM Thomas J. Watson Res. Center, Yorktown Heights, NY, USA
Volume :
16
Issue :
3
fYear :
1990
fDate :
3/1/1990 12:00:00 AM
Firstpage :
350
Lastpage :
359
Abstract :
Three aspects of the modeling of multiversion software are considered. First, the beta-binomial distribution is proposed for modeling correlated failures in multiversion software. Second, a combinatorial model for predicting the reliability of a multiversion software configuration is presented. This model can take as inputs failure distributions either from measurements or from a selected distribution (e.g. beta-binomial). Various recovery methods can be incorporated in this model. Third, the effectiveness of the community error recovery method based on checkpointing is investigated. This method appears to be effective only when the failure behaviors of program versions are lightly correlated. Two different types of checkpoint failure are also considered: an omission failure where the correct output is recognized at a checkpoint but the checkpoint fails to correct the wrong outputs and a destructive failure where the good versions get corrupted at a checkpoint
Keywords :
fault tolerant computing; software reliability; system recovery; beta-binomial distribution; checkpoint failure; checkpointing; combinatorial model; community error recovery; correlated failures; destructive failure; failure behaviors; failure distributions; lightly correlated; multiversion software; omission failure; recovery methods; selected distribution; software configuration; Checkpointing; Costs; Fault tolerance; Hardware; Parallel programming; Predictive models; Random variables; Software reliability; Software systems; Voting;
fLanguage :
English
Journal_Title :
Software Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0098-5589
Type :
jour
DOI :
10.1109/32.48942
Filename :
48942
Link To Document :
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