Cloud Infrastructures forIn SilicoDrug Discovery: Economic and Practical Aspects

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.

Delivering cloud services with QoS requirements: Business opportunities, architectural solutions and energy-saving aspects

“…In other real scenarios, we have considered [61], instead, clearly emerged more than remarkable difference amongst the three strategies for both the two metrics. Such differences was due to the kinds and characteristics of the applications considered (i.e.…”

“…Moreover the wide adoption of standards like the Java Portlet facilitates the development of highly customized but reusable interfaces. Such architectural solution will also allow us to exploit the feature of managing heavy demanding workflows to support applications in the bioinformatics and earth science domains, such as the ones we analyzed in [61] and [8]. As a further development we are going to investigate the possibility of migrating VMs between different Cloud infrastructures, to improve both energy savings as well performance of the submitted services.…”

Prediction of Potential Lead Molecules through Systematic Integration of Multi-omics Datasets - A Mini-Review

“…There are many cloud servers such as Cloud BLAST [30], Myrna [31], Cloud Burst [32], Hadoop-BAM [33], GPU-BLAST [34], Hydra [35], Peak Ranger [36],Crossbow [37], etc. were available over cloud for analyzing different types of big datasets [38][39][40][41].…”

Soft Computing Techniques for the Protein Folding Problem on High Performance Computing Architectures

“…Cloud computing platforms are very interesting for bioinformatics practitioners mainly for the flexibility and the cost-effectiveness. Truth be told, this actually depends on the workloads they expect to run on the cloud but, in general, small-medium bioinformatics laboratories, which may perform bioinformatics analysis are moving to this technology as they avoid cost and issues of having an inhouse computer infrastructure [54]. An alternative solution is represented by Hybrid Clouds that have both the scalability offered by cloud computing and the control and ad-hoc customizations supplied by in-house computers [55].…”