DocumentCode :
3678407
Title :
GRAPH/Z: A Key-Value Store Based Scalable Graph Processing System
Author :
Tonglin Li; Chaoqi Ma; Jiabao Li; Xiaobing Zhou; Ke Wang; Dongfang Zhao;Iman Sadooghi;Ioan Raicu
fYear :
2015
Firstpage :
516
Lastpage :
517
Abstract :
The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-the-art graph processing systems and databases usually adopt master/slave architecture that potentially impairs their The contributions of this paper are as follows: scalability. This work describes the design and implementation of a new graph processing system based on Bulk Synchronous Parallel model. Our system is built on top of ZHT, a scalable distributed key-value store, which benefits the graph processing in terms of scalability, performance and persistency. The experiment results imply excellent scalability.
Keywords :
"Yttrium","Conferences","Big data","Computational modeling","Loading","Scalability","Databases"
Publisher :
ieee
Conference_Titel :
Cluster Computing (CLUSTER), 2015 IEEE International Conference on
Type :
conf
DOI :
10.1109/CLUSTER.2015.90
Filename :
7307637
Link To Document :
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