DocumentCode :
1666733
Title :
Greft: Arbitrary Fault-Tolerant Distributed Graph Processing
Author :
Presser, Daniel ; Lau Cheuk Lung ; Correia, Miguel
Author_Institution :
Dept. de Inf. e Estatistica, Univ. Fed. de Santa Catarina (UFSC), Florianopolis, Brazil
fYear :
2015
Firstpage :
452
Lastpage :
459
Abstract :
Many large-scale computing problems can be modeled as graphs. Example areas include the web, social networks, and biological systems. The increasing sizes of datasets has led to the creation of various distributed large scale graph processing systems, e.g., Google Pregel. Although these systems tolerate crash faults, the literature suggests they are vulnerable to a wider range of accidental arbitrary faults (also called Byzantine faults). In this paper we present an algorithm and a prototype of a distributed large-scale graph processing system that can tolerate arbitrary faults. The prototype is based on GPS, an open source implementation of Pregel. Experimental results of the prototype in Amazon AWS are presented, showing that it uses only twice the resources of the original implementation, instead of 3-4 times as usual in Byzantine fault-tolerant systems. This cost may be acceptable for critical applications that require this level of fault tolerance.
Keywords :
distributed processing; fault tolerant computing; graph theory; Amazon AWS; Byzantine fault-tolerant system; GPS; Google Pregel; Greft; World Wide Web; accidental arbitrary fault; biological system; crash fault; distributed large scale graph processing system; distributed large-scale graph processing system; fault-tolerant distributed graph processing; large-scale computing problem; open source implementation; social network; Computer architecture; Computer crashes; Fault tolerance; Fault tolerant systems; Global Positioning System; Partitioning algorithms; Prototypes; Byzantine fault-tolerance; distributed graph processing; fault-tolerance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Big Data (BigData Congress), 2015 IEEE International Congress on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-7277-0
Type :
conf
DOI :
10.1109/BigDataCongress.2015.73
Filename :
7207257
Link To Document :
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