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CAD: An Efficient Data Management and Migration Scheme across Clouds for Data-Intensive Scientific Applications

  • Ching-Hsien Hsu
  • Alfredo Cuzzocrea
  • Shih-Chang Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6864)

Abstract

Data management and migration are important research challenges of novel Cloud environments. While moving data among different geographical domains, it is important to lower the transmission cost for performance purposes. Efficient scheduling methods allow us to manage data transmissions with lower number of steps and shorter transmission time. In previous research efforts, several methods have been proposed in literature in order to manage data and minimize transmission cost for the case of Single Cluster environments. Unfortunately, these methods are not suitable to large-scale and complicated environments such as Clouds, with particular regard to the case of scheduling policies. Starting from these motivations, in this paper we propose an efficient data transmission method for data-intensive scientific applications over Clouds, called Cloud Adaptive Dispatching (CAD). This method adapts to specialized characteristics of Cloud systems and successfully shortens the transmission cost, while also avoiding node contention during moving data from sites to sites. We conduct an extensive campaign of experiments focused to test the effective performance of CAD. Results clearly demonstrate the improvements offered by CAD in supporting data transmissions across Clouds for data-intensive scientific applications.

Keywords

Schedule Algorithm Cloud Environment Migration Scheme Transmission Cost Transmission Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Ching-Hsien Hsu
    • 1
  • Alfredo Cuzzocrea
    • 2
  • Shih-Chang Chen
    • 1
  1. 1.Chung Hua UniversityTaiwan
  2. 2.ICAR-CNR and University of CalabriaItaly

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