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. 2013:2013:138012.
doi: 10.1155/2013/138012. Epub 2013 Sep 10.

Cloud infrastructures for in silico drug discovery: economic and practical aspects

Daniele D'Agostino  1 , Andrea Clematis, Alfonso Quarati, Daniele Cesini, Federica Chiappori, Luciano Milanesi, Ivan Merelli
Affiliations

Cloud infrastructures for in silico drug discovery: economic and practical aspects

Daniele D'Agostino et al. Biomed Res Int. 2013.

Abstract

Cloud computing opens new perspectives for small-medium biotechnology laboratories that need to perform bioinformatics analysis in a flexible and effective way. This seems particularly true for hybrid clouds that couple the scalability offered by general-purpose public clouds with the greater control and ad hoc customizations supplied by the private ones. A hybrid cloud broker, acting as an intermediary between users and public providers, can support customers in the selection of the most suitable offers, optionally adding the provisioning of dedicated services with higher levels of quality. This paper analyses some economic and practical aspects of exploiting cloud computing in a real research scenario for the in silico drug discovery in terms of requirements, costs, and computational load based on the number of expected users. In particular, our work is aimed at supporting both the researchers and the cloud broker delivering an IaaS cloud infrastructure for biotechnology laboratories exposing different levels of nonfunctional requirements.

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Figures

Figure 1
Figure 1
Principal operations of a typical drug discovery pipeline.
Figure 2
Figure 2
UML sequential diagram of a request for service execution in a hybrid cloud.
Figure 3
Figure 3
The brokering system architecture.
Figure 4
Figure 4
Private zone utilization U at varying arrival rates.
Figure 5
Figure 5
Annual CB's revenue.
Figure 6
Figure 6
Average waiting times, in hours, per pipeline.
Figure 7
Figure 7
Private zone utilization U with 75% sporadic users.
Figure 8
Figure 8
Annual CB's revenue with 75% sporadic users.
Figure 9
Figure 9
Average waiting times per pipeline with 75% sporadic users.
Figure 10
Figure 10
Private zone utilization U with 75% frequent users.
Figure 11
Figure 11
Annual CB's revenue with 75% frequent users.
Figure 12
Figure 12
Average waiting times per pipeline with 75% frequent users.
Figure 13
Figure 13
Private zone utilization U, with a 15-node system.
Figure 14
Figure 14
Annual CB's revenue with a 15-node system.
Figure 15
Figure 15
Percentage revenue increases with respect to the basic scenario (15 versus 10 nodes).
Figure 16
Figure 16
Average waiting times per pipeline with a 15-node system.
See this image and copyright information in PMC

References

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