By Topic

A Local-Optimisation Based Strategy for Cost-Effective Datasets Storage of Scientific Applications in the Cloud

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Dong Yuan ; Fac. of Inf. & Commun. Technol., Swinburne Univ. of Technol., Melbourne, VIC, Australia ; Yun Yang ; Xiao Liu ; Jinjun Chen

Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application datasets can be stored in the cloud. However, due to the pay-as-you-go model, the datasets should be strategically stored in order to reduce the overall application cost. In this paper, by utilising Data Dependency Graph (DDG) from data provenances in scientific applications, deleted datasets can be regenerated, and as such we develop a novel cost-effective datasets storage strategy that can automatically store appropriate datasets in the cloud. This strategy achieves a localised optimal trade-off between computation and storage, meanwhile also taking users' tolerance of data accessing delay into consideration. Simulations conducted on general (random) datasets and a specific astrophysics pulsar searching application with Amazon's cost model show that our strategy can reduce the application cost significantly.

Published in:

Cloud Computing (CLOUD), 2011 IEEE International Conference on

Date of Conference:

4-9 July 2011