Large-scale distributed graph computing systems: An experimental evaluation

Proceedings of the VLDB Endowment

Volumn 8, Issue 3, 2014, Pages 281-292

  Lu, Yi ^a   Cheng, James ^a   Yan, Da ^a   Wu, Huanhuan ^a  

^a CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

Author keywords

[No Author keywords available]

Indexed keywords

DISTRIBUTED COMPUTER SYSTEMS; SOCIAL SCIENCES COMPUTING;

COMPUTING SYSTEM; EXISTING SYSTEMS; EXPERIMENTAL EVALUATION; GRAPH DATA; LARGE-SCALES; MOBILE PHONE NETWORKS; PERFORMANCE; PREGELS; REAL-WORLD; WEB GRAPHS;

COMPUTATION THEORY;

EID: 84937437181     PISSN: None     EISSN: 21508097     Source Type: Journal    
DOI: 10.14778/2735508.2735517     Document Type: Conference Paper

References (28)

1. T. E. Anderson, S. S. Owicki, J. B. Saxe, and C. P. Thacker. High speed switch scheduling for local area networks. ACM Trans. Comput. Syst., 11(4):319-352, 1993.
2. Apache Giraph. http://giraph.apache.org/.
3. D. A. Bader and K. Madduri. GTgraph: A synthetic graph generator suite. Atlanta, GA, February, 2006.
4. Y. Bu. Pregelix: dataflow-based big graph analytics. In SoCC, page 54, 2013.
5. P. Flajolet and G. N. Martin. Probabilistic counting algorithms for data base applications. J. Comput. Syst. Sci., 31(2):182-209, 1985.
6. A. H. Gebremedhin and F. Manne. Scalable parallel graph coloring algorithms. Concurrency - Practice and Experience, 12(12):1131-1146, 2000.
7. J. E. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin. PowerGraph: Distributed graph-parallel computation on natural graphs. In OSDI, pages 17-30, 2012.
8. Y. Guo, M. Biczak, A. L. Varbanescu, A. Iosup, C. Martella, and T. L. Willke. How well do graph-processing platforms perform? an empirical performance evaluation and analysis. In IPDPS, 2014.
9. M. Han, K. Daudjee, K. Ammar, M. T. Özsu, X. Wang, and T. Jin. An experimental comparison of pregel-like graph processing systems. PVLDB, 7(12):1047-1058, 2014.
10. U. Kang, C. E. Tsourakakis, A. P. Appel, C. Faloutsos, and J. Leskovec. Hadi: mining radii of large graphs. TKDD, 5(2):8, 2011.
11. Z. Khayyat, K. Awara, A. Alonazi, H. Jamjoom, D. Williams, and P. Kalnis. Mizan: a system for dynamic load balancing in large-scale graph processing. In EuroSys, pages 169-182, 2013.
12. A. Kyrola, G. E. Blelloch, and C. Guestrin. GraphChi: Large-scale graph computation on just a PC. In OSDI, pages 31-46, 2012.
13. Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin, and J. M. Hellerstein. Distributed GraphLab: A framework for machine learning in the Cloud. PVLDB, 5(8):716-727, 2012.
14. Y. Lu, J. Cheng, D. Yan, and H. Wu. Large-scale distributed graph computing systems: An experimental evaluation. CUHK Technical Report (http://www.cse.cuhk.edu.hk/pregelplus/exp/TR.pdf), 2014.
15. G. Malewicz, M. H. Austern, A. J. C. Bik, J. C. Dehnert, I. Horn, N. Leiser, and G. Czajkowski. Pregel: a system for large-scale graph processing. In SIGMOD Conference, pages 135-146, 2010.
16. S. Nobari, X. Lu, P. Karras, and S. Bressan. Fast random graph generation. In EDBT, pages 331-342, 2011.
17. C. R. Palmer, P. B. Gibbons, and C. Faloutsos. ANF: a fast and scalable tool for data mining in massive graphs. In KDD, pages 81-90, 2002.
18. V. Rastogi, A. Machanavajjhala, L. Chitnis, and A. D. Sarma. Finding connected components in MapReduce in logarithmic rounds. In ICDE, pages 50-61, 2013.
19. S. Salihoglu and J. Widom. GPS: a graph processing system. In SSDBM, pages 22:1-22:12, 2013.
20. S. Salihoglu and J. Widom. Optimizing graph algorithms on Pregel-like systems. PVLDB, 7(7):577-588, 2014.
21. N. Satish, N. Sundaram, M. M. A. Patwary, J. Seo, J. Park, M. A. Hassaan, S. Sengupta, Z. Yin, and P. Dubey. Navigating the maze of graph analytics frameworks using massive graph datasets. In SIGMOD Conference, pages 979-990, 2014.
22. Y. Shiloach and U. Vishkin. An O(log n) parallel connectivity algorithm. J. Algorithms, 3(1):57-67, 1982.
23. Y. Tian, A. Balmin, S. A. Corsten, S. Tatikonda, and J. McPherson. From "think like a vertex" to "think like a graph". PVLDB, 7(3):193-204, 2013.
24. R. S. Xin, J. E. Gonzalez, M. J. Franklin, and I. Stoica. GraphX: a resilient distributed graph system on Spark. In GRADES, page 2, 2013.
25. D. Yan, J. Cheng, Y. Lu, and W. Ng. Blogel: A block-centric framework for distributed computation on real-world graphs. PVLDB, 7(14), 2014.
26. D. Yan, J. Cheng, Y. Lu, and W. Ng. Pregel+: Technical report. (http://www.cse.cuhk.edu.hk/pregelplus/pregelplus.pdf), 2014.
27. D. Yan, J. Cheng, K. Xing, Y. Lu, W. Ng, and Y. Bu. Pregel algorithms for graph connectivity problems with performance guarantees. PVLDB, 7(14), 2014.
28. M. Zaharia, M. Chowdhury, T. Das, A. Dave, J. Ma, M. McCauly, M. J. Franklin, S. Shenker, and I. Stoica. Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing. In NSDI, pages 15-28, 2012.