6 research outputs found
VM-MAD: a cloud/cluster software for service-oriented academic environments
The availability of powerful computing hardware in IaaS clouds makes cloud
computing attractive also for computational workloads that were up to now
almost exclusively run on HPC clusters.
In this paper we present the VM-MAD Orchestrator software: an open source
framework for cloudbursting Linux-based HPC clusters into IaaS clouds but also
computational grids. The Orchestrator is completely modular, allowing flexible
configurations of cloudbursting policies. It can be used with any batch system
or cloud infrastructure, dynamically extending the cluster when needed. A
distinctive feature of our framework is that the policies can be tested and
tuned in a simulation mode based on historical or synthetic cluster accounting
data.
In the paper we also describe how the VM-MAD Orchestrator was used in a
production environment at the FGCZ to speed up the analysis of mass
spectrometry-based protein data by cloudbursting to the Amazon EC2. The
advantages of this hybrid system are shown with a large evaluation run using
about hundred large EC2 nodes.Comment: 16 pages, 5 figures. Accepted at the International Supercomputing
Conference ISC13, June 17--20 Leipzig, German
CloudMan as a platform for tool, data, and analysis distribution
Background Cloud computing provides an infrastructure that facilitates large scale computational analysis in a scalable, democratized fashion, However, in this context it is difficult to ensure sharing of an analysis environment and associated data in a scalable and precisely reproducible way. Results CloudMan (usecloudman.org) enables individual researchers to easily deploy, customize, and share their entire cloud analysis environment, including data, tools, and configurations. Conclusions With the enabled customization and sharing of instances, CloudMan can be used as a platform for collaboration. The presented solution improves accessibility of cloud resources, tools, and data to the level of an individual researcher and contributes toward reproducibility and transparency of research solutions
Genomics Virtual Laboratory: a practical bioinformatics workbench for the cloud
Analyzing high throughput genomics data is a complex and compute intensive task, generally requiring numerous software tools and large reference data sets, tied together in successive stages of data transformation and visualisation. A computational platform enabling best practice genomics analysis ideally meets a number of requirements, including: a wide range of analysis and visualisation tools, closely linked to large user and reference data sets ; workflow platform(s) enabling accessible, reproducible, portable analyses, through a flexible set of interfaces ; highly available, scalable computational resources ; and flexibility and versatility in the use of these resources to meet demands and expertise of a variety of users. Access to an appropriate computational platform can be a significant barrier to researchers, as establishing such a platform requires a large upfront investment in hardware, experience, and expertise
An evaluation of galaxy and ruffus-scripting workflows system for DNA-seq analysis
>Magister Scientiae - MScFunctional genomics determines the biological functions of genes on a global scale by
using large volumes of data obtained through techniques including next-generation
sequencing (NGS). The application of NGS in biomedical research is gaining in
momentum, and with its adoption becoming more widespread, there is an increasing
need for access to customizable computational workflows that can simplify, and offer
access to, computer intensive analyses of genomic data. In this study, the Galaxy and
Ruffus frameworks were designed and implemented with a view to address the
challenges faced in biomedical research. Galaxy, a graphical web-based framework,
allows researchers to build a graphical NGS data analysis pipeline for accessible,
reproducible, and collaborative data-sharing. Ruffus, a UNIX command-line framework
used by bioinformaticians as Python library to write scripts in object-oriented style,
allows for building a workflow in terms of task dependencies and execution logic. In
this study, a dual data analysis technique was explored which focuses on a comparative
evaluation of Galaxy and Ruffus frameworks that are used in composing analysis
pipelines. To this end, we developed an analysis pipeline in Galaxy, and Ruffus, for the
analysis of Mycobacterium tuberculosis sequence data. Furthermore, this study aimed
to compare the Galaxy framework to Ruffus with preliminary analysis revealing that the
analysis pipeline in Galaxy displayed a higher percentage of load and store instructions.
In comparison, pipelines in Ruffus tended to be CPU bound and memory intensive. The
CPU usage, memory utilization, and runtime execution are graphically represented in
this study. Our evaluation suggests that workflow frameworks have distinctly different
features from ease of use, flexibility, and portability, to architectural designs