TY - GEN
T1 - On characterizing the performance of distributed graph computation platforms
AU - Barnawi, Ahmed
AU - Batarfi, Omar
AU - Behteshi, Seyed Mehdi Reza
AU - Elshawi, Radwa
AU - Fayoumi, Ayman
AU - Nouri, Reza
AU - Sakr, Sherif
PY - 2015
Y1 - 2015
N2 - Graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. Therefore, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In practice, distributed processing of large scale graphs is a challenging task due to their size in addition to their inherent irregular structure and the iterative nature of graph processing and computation algorithms. In recent years, several distributed graph processing systems have been presented, most notably Pregel and GraphLab, to tackle this challenge. In particular, both systems use a vertex-centric computation model which enables the user to design a program that is executed locally for each vertex in parallel. In this paper, we analyze the performance characteristics of distributed graph processing systems and provide an experimental comparison on the performance of two popular systems in this area.
AB - Graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. Therefore, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In practice, distributed processing of large scale graphs is a challenging task due to their size in addition to their inherent irregular structure and the iterative nature of graph processing and computation algorithms. In recent years, several distributed graph processing systems have been presented, most notably Pregel and GraphLab, to tackle this challenge. In particular, both systems use a vertex-centric computation model which enables the user to design a program that is executed locally for each vertex in parallel. In this paper, we analyze the performance characteristics of distributed graph processing systems and provide an experimental comparison on the performance of two popular systems in this area.
UR - http://www.scopus.com/inward/record.url?scp=84922346226&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-15350-6_3
DO - 10.1007/978-3-319-15350-6_3
M3 - Conference proceeding contribution
AN - SCOPUS:84922346226
SN - 9783319153490
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 29
EP - 43
BT - Performance Characterization and Benchmarking
A2 - Nambiar, Raghunath
A2 - Poess, Meikel
PB - Springer, Springer Nature
CY - Cham, Switzerland
T2 - 6th TPC Technology Conference on Performance Evaluation and Benchmarking, TPCTC 2014 held in conjunction with 40th International Conference on Very Large Data Bases, VLDB 2014
Y2 - 1 September 2014 through 5 September 2014
ER -