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