Paper Info
Title
Beehive: A Framework for Graph Data Analytics on Cloud Computing Platforms
Abstract
Beehive is a parallel programming framework designed for cluster-based computing environments in cloud data centers. It is specifically targeted for graph data analysis problems. The Beehive framework provides the abstraction of key-value based global object storage, which is maintained in memory of the cluster nodes. Its computation model is based on optimistic concurrency control in executing concurrent tasks as atomic transactions for harnessing amorphous parallelism in graph analysis problems. We describe here the architecture and the programming abstractions provided by this framework, and present the performance of the Beehive framework for several graph problems such as maximum flow, minimum weight spanning tree, graph coloring, and the PageRank algorithm.
Year
DOI
Venue
2014
10.1109/ICPPW.2014.50
Parallel Processing Workshops
Keywords
Field
DocType
cloud computing,concurrency control,graph colouring,parallel programming,trees (mathematics),beehive framework,pagerank algorithm,amorphous parallelism,atomic transaction,cloud data center,cluster-based computing,graph analysis problem,graph coloring,graph data analytics,key-value based global object storage,maximum flow,minimum weight spanning tree,optimistic concurrency control,programming abstraction,graph algorithms,transactional memory,data models,servers,computational modeling,instruction sets,parallel processing,data analysis,programming
Data modeling,Object storage,Computer science,Parallel computing,Transactional memory,Theoretical computer science,Power graph analysis,Spanning tree,Optimistic concurrency control,Cloud computing,Graph coloring,Distributed computing
Conference
ISSN
Citations 
PageRank 
1530-2016
1
0.36
References 
Authors
13
3
Name
Order
Citations
PageRank
Anand Tripathi11151106.92
Vinit Padhye2183.10
Tara Sasank Sunkara320.71