CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications

Matsunaga, Andrea; Tsugawa, Mauricio; Fortes, José A. B.

doi:10.1109/escience.2008.62

Cited by 252 publications

(136 citation statements)

References 13 publications

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“…Moretti et al has reported a similar observation that MapReduce is not sufficient to express all-to-all style computation [43]. The existing MapReduce BLAST implementation, i.e., CloudBLAST [44], only implements query segmentation and stores the entire database on each node. As we discussed in Section 2.2, such a design is not scalable to large databases.…”

Section: Mapreducementioning

confidence: 88%

Coordinating Computation and I/O in Massively Parallel Sequence Search

Lin

Feng

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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Abstract-With the explosive growth of genomic information, the searching of sequence databases has emerged as one of the most computation-and data-intensive scientific applications. Our previous studies suggested that parallel genomic sequence-search possesses highly irregular computation and I/O patterns. Effectively addressing these run-time irregularities is thus the key to designing scalable sequence-search tools on massively parallel computers. While the computation scheduling for irregular scientific applications and the optimization of noncontiguous file accesses have been well studied independently, little attention has been paid to the interplay between the two. In this paper, we systematically investigate the computation and I/O scheduling for data-intensive, irregular scientific applications within the context of genomic sequence search. Our study reveals that the lack of coordination between computation scheduling and I/O optimization could result in severe performance issues. We then propose an integrated scheduling approach that effectively improves sequence-search throughput by gracefully coordinating the dynamic load-balancing of computation and highperformance noncontiguous I/O.

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Section: Mapreducementioning

confidence: 88%

Coordinating Computation and I/O in Massively Parallel Sequence Search

Lin

Feng

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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“…have implemented REST ful API web services or SOAP to share or integrate data in the form of FTP, HTML, XML, JSON, plain text, or AWK commands [29].Moreover, cloud computing services were offered to handle, analyze, or interpret big datasets through various remote applications/servers. 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].…”

Section: Comprehensive Data Integration Methodsmentioning

confidence: 99%

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

2017

IJCRR

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Prediction of a novel or potential lead molecules for a therapeutic drug target without adverse effects is a challenging task in the drug designing, discovery, and development process. The systematic integration of multi-omics data from various data/knowledge bases through computational techniques enables to identify potential lead molecules and study the therapeutic properties. Over the last decades, several drug discoveries using multi-omics and huge dataset integration methods proven with successive results. In this paper, we present different types of computational approaches for prediction of potential lead molecules through the systems-level integration of multi-omics datasets.

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“…For example, Matsunaga et al [22] have shown that multi-cloud BLAST computations with MapReduce can scale almost linearly.…”

Section: Multi-cloud Job Flowsmentioning

confidence: 99%

Resilin: Elastic MapReduce over Multiple Clouds

Iordache

Morin

Parlavantzas

et al. 2013

2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing

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Abstract:The MapReduce programming model, introduced by Google, offers a simple and efficient way of performing distributed computation over large data sets. Although Google's implementation is proprietary, MapReduce can be leveraged by anyone using the free and open-source Apache Hadoop framework. To simplify the usage of Hadoop in the cloud, Amazon Web Services offers Elastic MapReduce, a web service enabling users to run MapReduce jobs. Elastic MapReduce takes care of resource provisioning, Hadoop configuration and performance tuning, data staging, fault tolerance, etc. This service drastically reduces the entry barrier to perform MapReduce computations in the cloud, allowing users to concentrate on the problem to solve. However, Elastic MapReduce is restricted to Amazon EC2 resources, and is provided at an additional cost. In this paper, we present Resilin, a system implementing the Elastic MapReduce API with resources from clouds other than Amazon EC2, such as private and scientific clouds. Furthermore, we explore a feature going beyond the current Amazon Elastic MapReduce offering: performing MapReduce computations over multiple distributed clouds. The evaluation of Resilin shows the benefits of running computations on more than one cloud. While not being the most efficient way to perform Hadoop computations, it solves the problem of resource availability and adds more flexibility regarding the type/price of resource.

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CloudBLAST: Combining MapReduce and Virtualization on Distributed Resources for Bioinformatics Applications

Cited by 252 publications

References 13 publications

Coordinating Computation and I/O in Massively Parallel Sequence Search

Coordinating Computation and I/O in Massively Parallel Sequence Search

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

Resilin: Elastic MapReduce over Multiple Clouds

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