Beehive: A Framework for Graph Data Analytics on Cloud Computing Platforms

Anand Tripathi, Vinit Padhye, Tara Sasank Sunkara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 43rd International Conference on Parallel Processing Workshops, ICPPW 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages331-338
Number of pages8
ISBN (Electronic)9781479956159
DOIs
StatePublished - May 7 2015
Event43rd International Conference on Parallel Processing Workshops, ICPPW 2014 - Minneapolis, United States
Duration: Sep 9 2014Sep 12 2014

Publication series

NameProceedings of the International Conference on Parallel Processing Workshops
Volume2015-May
ISSN (Print)1530-2016

Other

Other43rd International Conference on Parallel Processing Workshops, ICPPW 2014
CountryUnited States
CityMinneapolis
Period9/9/149/12/14

Keywords

  • Cloud computing
  • Graph algorithms
  • Optimistic concurrency control
  • Parallel programming
  • Transactional memory

Fingerprint Dive into the research topics of 'Beehive: A Framework for Graph Data Analytics on Cloud Computing Platforms'. Together they form a unique fingerprint.

Cite this