An Analysis of Distributed Programming Models and Frameworks for Large-scale Graph Processing

References

• J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Commun. ACM, Vol. 51, no. 1, pp. 107–113, 2008. doi: 10.1145/1327452.1327492
   Web of Science ®Google Scholar
• 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 Proceedings of the 2010 International Conference on Management of Data (SIGMOD '10), Indianapolis, IN, USA, 2010, pp. 135–146.
   Google Scholar
• J. E. Gonzalez, Y. Low, H. Gu, D. Bickson and C. Guestrin, “PowerGraph: Distributed graph-parallel computation on natural graphs,” in Proceedings of the 10th USENIX conference on Operating Systems Design and Implementation (OSDI'12), Hollywood, CA, USA, 2012, pp. 17–30.
   Google Scholar
• Y. Low, D. Bickson, J. Gonzalez, C. Guestrin, A. Kyrola and J. M. Hellerstein, “Distributed GraphLab: A framework for machine learning and data mining in the cloud,” Proc. VLDB Endowment, Vol. 5, no. 8, pp. 716–727, 2012. doi: 10.14778/2212351.2212354
   Google Scholar
• M. Zaharia, “Resilient distributed datasets: A fault-tolerant abstraction for in-memory cluster computing,” in Proceedings of the 9th USENIX conference on Networked Systems Design and Implementation (NSDI'12), San Jose, CA, USA, 2012, pp. 2–2.
   Google Scholar
• R. S. Xin, J. E. Gonzalez, M. J. Franklin and I. Stoica, “GraphX: A resilient distributed graph system on Spark,” in Proceedings of the 1st International Workshop on Graph Data Management Experiences and Systems (GRADES '13), New York, NY, USA, 2013, pp. 2:1–2:6.
   Google Scholar
• C. Mateos, A. Zunino and M. Campo, “A survey on approaches to gridification,” Softw.: Pract. Exp., Vol. 38, no. 5, pp. 523–556, 2008.
   Web of Science ®Google Scholar
• L. G. Valiant, “A bridging model for parallel computation,” Commun. ACM, Vol. 33, no. 8, pp. 103–111, 1990. doi: 10.1145/79173.79181
   Web of Science ®Google Scholar
• B. Bahmani, K. Chakrabarti and D. Xin, “Fast personalized PageRank on MapReduce,” in Proceedings of the 2011 ACM SIGMOD International Conference on Management of Data (SIGMOD '11), Athens, Greece, 2011, pp. 973–984. ACM.
   Google Scholar
• L. Cao, B. Cho, H. D. Kim, Z. Li, M.-H. Tsai and I. Gupta, “Delta-SimRank computing on MapReduce,” in Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications (BigMine '12), Beijing, China, 2012, pp. 28–35. ACM.
   Google Scholar
• U. Kang, H. Tong, J. Sun, C.-Y. Lin and C. Faloutsos, “GBASE: A scalable and general graph management system,” in Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'11), 2011, pp. 1091–1099. ACM.
   Google Scholar
• U. Kang, B. Meeder, E. E. Papalexakis and C. Faloutsos, “Heigen: Spectral analysis for billion-scale graphs,” IEEE Trans. Knowl. Data Eng., Vol. 26, no. 2, pp. 350–362, 2014. doi: 10.1109/TKDE.2012.244
   Web of Science ®Google Scholar
• U. Kang, C. E. Tsourakakis and C. Faloutsos, “Pegasus: A peta-scale graph mining system implementation and observations,” in 2009 Ninth IEEE International Conference on Data Mining, Dec 2009, pp. 229–238.
   Google Scholar
• L. Page, S. Brin, R. Motwani and T. Winograd, “The PageRank citation ranking: Bringing order to the Web,” Tech. Rep. 1999-66, Stanford InfoLab, 1999.
   Google Scholar
• R. R. McCune, T. Weninger and G. Madey, “Thinking like a vertex: A survey of vertex-centric frameworks for distributed graph processing,” Distributed, Parallel, and Cluster Computing, Vol. 46556, 2015.
   Google Scholar
• M. Han and K. Daudjee, “Giraph unchained: Barrierless asynchronous parallel execution in Pregel-like graph processing systems,” Proc. VLDB Endowment, Vol. 8, no. 9, pp. 950–961, 2015. doi: 10.14778/2777598.2777604
   Web of Science ®Google Scholar
• B. Shao, H. Wang and Y. Li, “The trinity graph engine,” Tech. Rep. MSR-TR-2012-30, Microsoft Research, March 2012.
   Google Scholar
• E. Krepska, T. Kielmann, W. Fokkink and H. Bal, “HipG: Parallel processing of large-scale graphs,” ACM SIGOPS Oper. Syst. Rev., Vol. 45, no. 2, pp. 3–13, 2011. doi: 10.1145/2007183.2007185
   Google Scholar
• M. Han, K. Daudjee, K. Ammar, M. T. Özsu, X. Wang and T. Jin, “An experimental comparison of pregel-like graph processing systems,” Proc. VLDB Endowment, Vol. 7, no. 12, pp. 1047–1058, 2014. doi: 10.14778/2732977.2732980
   Google Scholar
• R. S. Xin, D. Crankshaw, A. Dave, J. E. Gonzalez, M. J. Franklin and I. Stoica, “Graphx: Unifying data-parallel and graph-parallel analytics,” CoRR, Vol. abs/1402.2394, 2014.
   Google Scholar
• S. Aridhi and E. M. Nguifo, “Big graph mining: Frameworks and techniques,” Big Data Res., Vol. 6, pp. 1–10, 2016. doi: 10.1016/j.bdr.2016.07.002
   Web of Science ®Google Scholar
• T. Kajdanowicz, P. Kazienko and W. Indyk, “Parallel processing of large graphs,” Future Gener. Comput. Syst., Vol. 32, pp. 324–337, 2014. doi: 10.1016/j.future.2013.08.007
   Web of Science ®Google Scholar
• O. Batarfi, R. E. Shawi, A. G. Fayoumi, R. Nouri, S.-M.-R. Beheshti, A. Barnawi and S. Sakr, “Large scale graph processing systems: Survey and an experimental evaluation,” Cluster Computing, 2015, pp. 1189–1213.
   Google Scholar