Amazon Elastic Compute Cloud Research Articles

Modeling of Distributed File Systems for Practical Performance Analysis

Cloud computing has received significant attention recently. Delivering quality guaranteed services in clouds is highly desired. Distributed file systems (DFSs) are the key component of any cloud-scale data processing middleware. Evaluating the performance of DFSs is accordingly very important. To avoid cost for late life cycle performance fixes and architectural redesign, providing performance analysis before the deployment of DFSs is also particularly important. In this paper, we propose a systematic and practical performance analysis framework, driven by architecture and design models for defining the structure and behavior of typical master/slave DFSs. We put forward a configuration guideline for specifications of configuration alternatives of such DFSs, and a practical approach for both qualitatively and quantitatively performance analysis of DFSs with various configuration settings in a systematic way. What distinguish our approach from others is that 1) most of existing works rely on performance measurements under a variety of workloads/strategies, comparing with other DFSs or running application programs, but our approach is based on architecture and design level models and systematically derived performance models; 2) our approach is able to both qualitatively and quantitatively evaluate the performance of DFSs; and 3) our approach not only can evaluate the overall performance of a DFS but also its components and individual steps. We demonstrate the effectiveness of our approach by evaluating Hadoop distributed file system (HDFS). A series of real-world experiments on EC2 (Amazon Elastic Compute Cloud), Tansuo and Inspur Clusters, were conducted to qualitatively evaluate the effectiveness of our approach. We also performed a set of experiments of HDFS on EC2 to quantitatively analyze the performance and limitation of the metadata server of DFSs. Results show that our approach can achieve sufficient performance analysis. Similarly, the proposed approach could be also applied to evaluate other DFSs such as MooseFS, GFS, and zFS.

LSRN: A PARALLEL ITERATIVE SOLVER FOR STRONGLY OVER- OR UNDERDETERMINED SYSTEMS.

We describe a parallel iterative least squares solver named LSRN that is based on random normal projection. LSRN computes the min-length solution to min x∈ℝ n ‖Ax - b‖2, where A ∈ ℝ m × n with m ≫ n or m ≪ n, and where A may be rank-deficient. Tikhonov regularization may also be included. Since A is involved only in matrix-matrix and matrix-vector multiplications, it can be a dense or sparse matrix or a linear operator, and LSRN automatically speeds up when A is sparse or a fast linear operator. The preconditioning phase consists of a random normal projection, which is embarrassingly parallel, and a singular value decomposition of size ⌈γ min(m, n)⌉ × min(m, n), where γ is moderately larger than 1, e.g., γ = 2. We prove that the preconditioned system is well-conditioned, with a strong concentration result on the extreme singular values, and hence that the number of iterations is fully predictable when we apply LSQR or the Chebyshev semi-iterative method. As we demonstrate, the Chebyshev method is particularly efficient for solving large problems on clusters with high communication cost. Numerical results show that on a shared-memory machine, LSRN is very competitive with LAPACK's DGELSD and a fast randomized least squares solver called Blendenpik on large dense problems, and it outperforms the least squares solver from SuiteSparseQR on sparse problems without sparsity patterns that can be exploited to reduce fill-in. Further experiments show that LSRN scales well on an Amazon Elastic Compute Cloud cluster.

An innovative ocean planning tool for the Atlantic outer continental shelf: The EcoSpatial Information Database

Robust scientific information is essential to proper leasing decisions in offshore waters of the United States. This information develops the necessary understanding of the environment required to protect ecosystems during sustainable energy development offshore. Collection of existing scientific information can be a laborious and time-consuming process. In an effort to collate and evaluate scientific information in a more effective and timely manner, the Bureau of Ocean Energy Management (BOEM) created a system that would improve the speed with which environmental research is performed while maintaining scientific defensibility of the resulting decisions. The EcoSpatial Information Database (ESID, pronounced “ee-sid”), available at http://esid.boem.gov, makes relevant scientific literature and ecological data for the Outer Continental Shelf (OCS) of the U.S. Atlantic Coast readily accessible via advanced location and content data searches. A unique search protocol was developed to identify and scientifically screen thousands of scientific articles and environmental reports to identify scientific datasets in the disciplines of marine geology, water quality, pelagic ecology, and benthic ecology. A prioritization process culled the results to a total of 3108 resources spanning the years of 1884 to 2010. These resources were then geospatially referenced and incorporated into the ESID. A web-based Geographic Information System (GIS), hosted in the Amazon Elastic Compute Cloud (EC2), was developed to provide broad access to the data and supporting documents. The cloud-based ESID Web Application allows users to search by content and location, view citations and abstracts, export bibliographic entries and view and download documents. The system is designed to support virtually unlimited geographic and subject matter expansion and will streamline BOEM's efforts to produce the required National Environmental Policy Act documents in a more efficient manner.

Empirical Performance Assessment of Public Clouds Using System Level Benchmarks

Amazon's Elastic Compute Cloud (EC2) Service is one of the leading public cloud service providers and offers many different levels of service. This paper looks into evaluating the memory, central processing unit (CPU), and input/output I/O performance of two different tiers of hardware offered through Amazon's EC2. Using three distinct types of system benchmarks, the performance of the micro spot instance and the M1 small instance are measured and compared. In order to examine the performance and scalability of the hardware, the virtual machines are set up in a cluster formation ranging from two to eight nodes. The results show that the scalability of the cloud is achieved by increasing resources when applicable. This paper also looks at the economic model and other cloud services offered by Amazon's EC2, Microsoft's Azure, and Google's App Engine.

BioPig: a Hadoop-based analytic toolkit for large-scale sequence data

The recent revolution in sequencing technologies has led to an exponential growth of sequence data. As a result, most of the current bioinformatics tools become obsolete as they fail to scale with data. To tackle this 'data deluge', here we introduce the BioPig sequence analysis toolkit as one of the solutions that scale to data and computation. We built BioPig on the Apache's Hadoop MapReduce system and the Pig data flow language. Compared with traditional serial and MPI-based algorithms, BioPig has three major advantages: first, BioPig's programmability greatly reduces development time for parallel bioinformatics applications; second, testing BioPig with up to 500 Gb sequences demonstrates that it scales automatically with size of data; and finally, BioPig can be ported without modification on many Hadoop infrastructures, as tested with Magellan system at National Energy Research Scientific Computing Center and the Amazon Elastic Compute Cloud. In summary, BioPig represents a novel program framework with the potential to greatly accelerate data-intensive bioinformatics analysis.

Toward a web-based real-time radiation treatment planning system in a cloud computing environment

To exploit the potential dosimetric advantages of intensity modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT), an in-depth approach is required to provide efficient computing methods. This needs to incorporate clinically related organ specific constraints, Monte Carlo (MC) dose calculations, and large-scale plan optimization. This paper describes our first steps toward a web-based real-time radiation treatment planning system in a cloud computing environment (CCE). The Amazon Elastic Compute Cloud (EC2) with a master node (named m2.xlarge containing 17.1 GB of memory, two virtual cores with 3.25 EC2 Compute Units each, 420 GB of instance storage, 64-bit platform) is used as the backbone of cloud computing for dose calculation and plan optimization. The master node is able to scale the workers on an ‘on-demand’ basis. MC dose calculation is employed to generate accurate beamlet dose kernels by parallel tasks. The intensity modulation optimization uses total-variation regularization (TVR) and generates piecewise constant fluence maps for each initial beam direction in a distributed manner over the CCE. The optimized fluence maps are segmented into deliverable apertures. The shape of each aperture is iteratively rectified to be a sequence of arcs using the manufacture’s constraints. The output plan file from the EC2 is sent to the simple storage service. Three de-identified clinical cancer treatment plans have been studied for evaluating the performance of the new planning platform with 6 MV flattening filter free beams (40 × 40 cm2) from the Varian TrueBeamTM STx linear accelerator. A CCE leads to speed-ups of up to 14-fold for both dose kernel calculations and plan optimizations in the head and neck, lung, and prostate cancer cases considered in this study. The proposed system relies on a CCE that is able to provide an infrastructure for parallel and distributed computing. The resultant plans from the cloud computing are identical to PC-based IMRT and VMAT plans, confirming the reliability of the cloud computing platform. This cloud computing infrastructure has been established for a radiation treatment planning. It substantially improves the speed of inverse planning and makes future on-treatment adaptive re-planning possible.

Parallel stochastic systems biology in the cloud

The stochastic modelling of biological systems, coupled with Monte Carlo simulation of models, is an increasingly popular technique in bioinformatics. The simulation-analysis workflow may result computationally expensive reducing the interactivity required in the model tuning. In this work, we advocate the high-level software design as a vehicle for building efficient and portable parallel simulators for the cloud. In particular, the Calculus of Wrapped Components (CWC) simulator for systems biology, which is designed according to the FastFlow pattern-based approach, is presented and discussed. Thanks to the FastFlow framework, the CWC simulator is designed as a high-level workflow that can simulate CWC models, merge simulation results and statistically analyse them in a single parallel workflow in the cloud. To improve interactivity, successive phases are pipelined in such a way that the workflow begins to output a stream of analysis results immediately after simulation is started. Performance and effectiveness of the CWC simulator are validated on the Amazon Elastic Compute Cloud.

SU-E-T-18: GATE Monte Carlo Simulation in a Cloud Computing Environment

Purpose: The GEANT4-based GATE is a unique and powerful Monte Carlo (MC) platform, which provides a single code library allowing the simulation of several specific applications, e.g. PET, SPECT, CT, internal and external radiotherapy, and hadron therapy. However, its lengthy computing time hinders its routine use in the clinic. Reducing its computing time is therefore of great importance. Thus, a commercial cloud compute service is well suited for GATE MC simulation, both in terms of cost and efficiency. This study achieves a reliable and efficient execution of GATE MC simulation and provides execution frameworks to end-users. Methods: The GATE software was ported on a commercial compute cloud environment — Amazon Elastic Compute Cloud (EC2). Simulation data was initially loaded onto the master node, and then distributed among independent worker nodes. The filed output from EC2 was sent down to the Amazon Simple Storage Service (S3). The results were finally aggregated on the local computer for display and data analysis. The distributed implementation was executed using a photon beam interacting in a 40 cm by 40 cm by 40 cm water phantom and a four-head SPECT imaging as benchmarks. Results: A cloud computing environment led to increased calculation speed for the cases implemented in this study. The speed increase scaled approximately linearly with the number of nodes used for computing. The output of the cloud-based GATE MC simulation was identical to that produced by the single-threaded implementation, and was resilient to hardware failure, indicating the reliability of the cloud computing platform. The user-friendliness offered by the workflow implementation does not introduce significant overhead. Conclusion: A cloud computing infrastructure has been established for GATE MC simulation. It substantially improves the speed of simulation, and makes rapid MC simulation for imaging/radiotherapy application possible.

Performance evaluation of Amazon Elastic Compute Cloud for NASA high‐performance computing applications

AbstractCloud computing environments are now widely available and are being increasingly utilized for technical computing. They are also being touted for high‐performance computing (HPC) applications in science and engineering. For example, Amazon Elastic Compute Cloud (EC2) Services offers specialized Cluster Compute instance types to run HPC applications. In this paper, we compare the performance characteristics of two Amazon EC2 HPC instance types with that of National Aeronautics and Space Administration's (NASA) Pleiades supercomputer, an SGI® ICE™ cluster. For this study, we utilized the HPC Challenge kernels and the NAS Parallel Benchmarks along with four full‐scale applications from the repertoire of codes that are being used by NASA scientists and engineers. We compare the total runtime of these codes for varying number of cores. We also break out the computation and communication times for a subset of these applications to explore the effect of interconnect differences on the two systems. In general, the single node performance of the two platforms is equivalent. However, for most of the codes when scaling to larger core counts, the performance of the EC2 HPC instances generally lags that of Pleiades because of worse network performance of the former. In addition to analyzing application performance, we also briefly touch upon the overhead due to virtualization and the usability of cloud environments such as Amazon EC2. Published 2013. This article is a U.S. Government work and is in the public domain in the U.S.A.

Accelerating R‐based analytics on the cloud

SummaryThis paper addresses how the benefits of cloud‐based infrastructure can be harnessed for analytical workloads. Often, the software handling analytical workloads is not developed by a professional programmer but on an ad hoc basis by analysts in high‐level programming environments such as R or MATLAB. The goal of this research is to allow Analysts to take an analytical job that executes on their personal workstations and with minimum effort execute it on cloud infrastructure and manage both the resources and the data required by the job. If this can be facilitated gracefully, then the Analyst benefits from on‐demand resources, low maintenance cost and scalability of computing resources, all of which are offered by the cloud. In this paper, a Platform for Parallel R‐based Analytics on the Cloud (P2RAC) that is placed between an Analyst and a cloud infrastructure is proposed and implemented. P2RAC offers a set of command‐line tools for managing the resources, such as instances and clusters, the data and the execution of the software on the Amazon Elastic Computing Cloud infrastructure. Experimental studies are pursued using two parallel problems and the results obtained confirm the feasibility of employing P2RAC for solving large‐scale analytical problems on the cloud.Copyright © 2013 John Wiley & Sons, Ltd.

Plug into the Supercloud

Cloud computing is often compared to the power utility model, but today's cloud providers don't simply supply raw computing resources as a commodity; they also act as distributors, dictating cloud services that aren't compatible across providers. A supercloud is a cloud service distribution layer that's completely decoupled from the provider. Leveraging a nested paravirtualization layer called the Xen-Blanket, the supercloud maintains the control necessary to implement hypervisor-level services and management. Using the Xen-Blanket to transform various cloud provider services into a unified offering, the authors have deployed a supercloud across the Amazon Elastic Compute Cloud, an enterprise cloud, and Cornell University.

Energy-Efficient Virtual Machine Consolidation

A novel approach to virtual machine (VM) consolidation, based on energy-efficient storage migration and live VM migration, is implemented using Eucalyptus, an open source clone of the Amazon Elastic Compute Cloud. Several experiments demonstrate the potential energy savings.

Accelerating 3B single-molecule super-resolution microscopy with cloud computing

To the Editor: The recently invented technique of Bayesian localization microscopy1 relaxes the requirement for an extremely high signal-to-background ratio for single-molecule super-resolution microscopy2–5. It enables a standard wide-field fluorescence microscope to generate super-resolution images from the stochastic bleaching and blinking6,7 of fluorescence dyes (3B analysis). Although the new 3B microscopy has enormous potential to put the power of super-resolution imaging into the hands of average biologists, its underlying algorithm can be an impediment. Calculating an area of 50 × 50 pixels takes approximately a full day on a state-of-the-art desktop computer (Fig. 1a). For larger image areas, the need for massive computing resources makes the 3B analysis inaccessible to many labs and limits its scope of application. We report here a simple solution for everyone: the Amazon Elastic Compute Cloud (Amazon EC2). Figure 1 3B analysis using Amazon EC2 Aside from its flexible and cost-effective super-computing power, the greatest advantages of Amazon EC2 are its accessibility and ease of use. Normally, porting a computational code to a new system involves the configuration of a system-dependent software environment. The convoluted configuration process—not to mention the increased complexity for cluster computing—often discourages scientists without programming backgrounds. Use of Amazon EC2 significantly lowers this entry barrier. By packaging the executable program together with its running environment in a single image file known as the Amazon machine image (AMI), we can distribute 3B analysis to the public through the Amazon Marketplace. Hundreds of processes can run on instances, with each instance configured as an exact copy of the AMI. A user can launch massively parallel 3B processes on the Cloud from a local computer in just a few minutes (Fig. 1b and Supplementary Video 1). Large computing power allows data analysis without compromising on field of view. We tested Amazon EC2 using the photoactivatable (PA) mCherry–tagged podosome data from ref. 1 (Fig. 1c). The recorded image was divided into small mosaics of 12 × 12 pixels. The mosaics were analyzed in parallel and recombined to form a super-resolution image of 9.6 × 9.6 µm. Given the same computation time, an eight-thread 3.40-GHz Intel Core i7 desktop computer could analyze a region of 2.715 × 2.715 µm. The image generated from 100 virtual cores from Amazon EC2 was 12 times as large. An entry-level user of the Amazon Cloud may obtain up to 100 instances at about $0.02–$0.06 per instance hour. Depending on the market price, reconstruction of a 25 × 25–µm image costs less than $25. We strongly recommend the use of our bash scripts to continuously monitor the reconstructed image (Supplementary Software and tutorial therein). The calculation should be terminated only when the structrual features have converged, as otherwise potentially serious image artifacts may still be present. Researchers may apply for Amazon Web Service research grants for additional computing hours. We performed a test with 300 instances of 600 virtual cores. An image of PAmCherry-tagged tubulins in U2OS cells (from our own experiments) with the size of 150 pixels × 100 pixels × 1,500 frames could be reconstructed in only 210 min (Fig. 1d), whereas on a state-of-the-art desktop computer, this process would have taken 9 d. Cloud computing has been transforming the information technology industry through its elastic and flexible on-demand services. Although cloud computing has contributed to the success of many companies such as Dropbox and Netflix, until now it has been little used in biological research. Through this Correspondence, we hope to highlight its huge potential for the imaging community.

Cloud-Based Code Execution Framework for scientific problem solving environments

In this paper we present a novel Code Execution Framework that can execute code of different problem solving environments (PSE), such as MATLAB, R and Octave, in parallel. In many e-Science domains different specialists are working together and need to share data or even execute calculations using programs created by other persons. Each specialist may use a different problem solving environment and therefore the collaboration can become quite difficult. Our framework supports different cloud platforms, such as Amazon Elastic Compute Cloud (EC2) and Eucalyptus. Therefore it is possible to use hybrid cloud infrastructures, e.g. a private cloud based on Eucalyptus for general base-level computations using the available local resources and additionally a public Amazon EC2 for peaks and time-dependent calculations. Our approach is to provide a secure platform that supports multiple problem solving environments, execute code in parallel with different parameter sets using multiple cores or machines in a cloud environment, and support researchers in executing code, even if the required problem solving environment is not installed locally. Additionally, existing parallel resources can easily be utilized for ongoing scientific calculations. The framework has been validated by and used in our real project addressing large-scale breath analysis research. Its research-prototype version is available as a PaaS cloud service model. In the future researchers will be able to install this framework on their own cloud infrastructures.

Is the Same Instance Type Created Equal? Exploiting Heterogeneity of Public Clouds

Public cloud platforms might start with homogeneous hardware; nevertheless, because of inevitable hardware upgrades, or adding more capacity, the initial homogeneous platform will gradually evolve into heterogeneous as time passes by. The consequent performance heterogeneity is of concern to cloud users. In this paper, we evaluate performance variations from hardware heterogeneity and scheduling mechanisms of public clouds. Amazon Elastic Compute Cloud (Amazon EC2) and Rackspace Cloud are used as the representatives because of their relatively long record and wide usage among small and medium enterprises (SMEs). A comprehensive set of microbenchmarks and application-level macrobenchmarks have been used to investigate performance variation. Several major contributions have been made. First, we find out that heterogeneous hardware is a commonality among the relatively long-lasting cloud platforms, although the level of heterogeneity varies. Second, we observe that heterogeneous hardware is the primary culprit of performance variation of cloud platforms. Third, we discover that varied CPU acquisition percentages and different virtual machine scheduling mechanisms exacerbate the performance variation problem, especially for network related operations. Finally, based on the observations, we propose cost-saving approaches and analyze Nash equilibrium from cloud user perspective. By using a simple "trial-and-better" approach, i.e., keep good-performing instances and discard bad-performing instances, cloud users can achieve up to 30 percent cost saving.

Exploiting parallel R in the cloud with SPRINT.

Advances in DNA Microarray devices and next-generation massively parallel DNA sequencing platforms have led to an exponential growth in data availability but the arising opportunities require adequate computing resources. High Performance Computing (HPC) in the Cloud offers an affordable way of meeting this need. Bioconductor, a popular tool for high-throughput genomic data analysis, is distributed as add-on modules for the R statistical programming language but R has no native capabilities for exploiting multi-processor architectures. SPRINT is an R package that enables easy access to HPC for genomics researchers. This paper investigates: setting up and running SPRINT-enabled genomic analyses on Amazon's Elastic Compute Cloud (EC2), the advantages of submitting applications to EC2 from different parts of the world and, if resource underutilization can improve application performance. The SPRINT parallel implementations of correlation, permutation testing, partitioning around medoids and the multi-purpose papply have been benchmarked on data sets of various size on Amazon EC2. Jobs have been submitted from both the UK and Thailand to investigate monetary differences. It is possible to obtain good, scalable performance but the level of improvement is dependent upon the nature of the algorithm. Resource underutilization can further improve the time to result. End-user's location impacts on costs due to factors such as local taxation. Although not designed to satisfy HPC requirements, Amazon EC2 and cloud computing in general provides an interesting alternative and provides new possibilities for smaller organisations with limited funds.

From On-Premise Software to Cloud Services: The Impact of Cloud Computing on Enterprise Software Vendors´ Business Models

Cloud computing is an emerging paradigm that allows users to conveniently access computing resources as pay-per-use services. Whereas cloud offerings such as Amazon's Elastic Compute Cloud and Google Apps are rapidly gaining a large user base, enterprise software's migration towards the cloud is still in its infancy. For software vendors the move towardscloud solutions implies profound changes in their value-creation logic. Not only are they forced to deliver fully web-enabled solutions and to replace their license model with service fees, they also need to build the competencies to host and manage business-critical applications for their customers. This motivates our research, which investigates cloud computing's implications for enterprise software vendors' business models. From multiple case studies covering traditional and pure cloud providers, we find that moving from on-premise software to cloud services affects all business model components, that is, the customer value proposition, resource base, value configuration, and financial flows. It thus underpins cloud computing's disruptive nature in the enterprise software domain. By deriving two alternative business model configurations, SaaS and SaaS+PaaS, our research synthesizes the strategic choices for enterprise software vendors and provides guidelines for designing viable business models.

Rainbow: a tool for large-scale whole-genome sequencing data analysis using cloud computing.

BackgroundTechnical improvements have decreased sequencing costs and, as a result, the size and number of genomic datasets have increased rapidly. Because of the lower cost, large amounts of sequence data are now being produced by small to midsize research groups. Crossbow is a software tool that can detect single nucleotide polymorphisms (SNPs) in whole-genome sequencing (WGS) data from a single subject; however, Crossbow has a number of limitations when applied to multiple subjects from large-scale WGS projects. The data storage and CPU resources that are required for large-scale whole genome sequencing data analyses are too large for many core facilities and individual laboratories to provide. To help meet these challenges, we have developed Rainbow, a cloud-based software package that can assist in the automation of large-scale WGS data analyses.ResultsHere, we evaluated the performance of Rainbow by analyzing 44 different whole-genome-sequenced subjects. Rainbow has the capacity to process genomic data from more than 500 subjects in two weeks using cloud computing provided by the Amazon Web Service. The time includes the import and export of the data using Amazon Import/Export service. The average cost of processing a single sample in the cloud was less than 120 US dollars. Compared with Crossbow, the main improvements incorporated into Rainbow include the ability: (1) to handle BAM as well as FASTQ input files; (2) to split large sequence files for better load balance downstream; (3) to log the running metrics in data processing and monitoring multiple Amazon Elastic Compute Cloud (EC2) instances; and (4) to merge SOAPsnp outputs for multiple individuals into a single file to facilitate downstream genome-wide association studies.ConclusionsRainbow is a scalable, cost-effective, and open-source tool for large-scale WGS data analysis. For human WGS data sequenced by either the Illumina HiSeq 2000 or HiSeq 2500 platforms, Rainbow can be used straight out of the box. Rainbow is available for third-party implementation and use, and can be downloaded from http://s3.amazonaws.com/jnj_rainbow/index.html.

The HEPiX Virtualisation Working Group: Towards a Grid of Clouds

The use of virtual machine images, as for example with Cloud services such as Amazon's Elastic Compute Cloud, is attractive for users as they have a guaranteed execution environment, something that cannot today be provided across sites participating in computing grids such as the Worldwide LHC Computing Grid. However, Grid sites often operate within computer security frameworks which preclude the use of remotely generated images. The HEPiX Virtualisation Working Group was setup with the objective to enable use of remotely generated virtual machine images at Grid sites and, to this end, has introduced the idea of trusted virtual machine images which are guaranteed to be secure and configurable by sites such that security policy commitments can be met. This paper describes the requirements and details of these trusted virtual machine images and presents a model for their use to facilitate the integration of Grid- and Cloud-based computing environments for High Energy Physics.

CloudMap: A Cloud-Based Pipeline for Analysis of Mutant Genome Sequences

Whole genome sequencing (WGS) allows researchers to pinpoint genetic differences between individuals and significantly shortcuts the costly and time-consuming part of forward genetic analysis in model organism systems. Currently, the most effort-intensive part of WGS is the bioinformatic analysis of the relatively short reads generated by second generation sequencing platforms. We describe here a novel, easily accessible and cloud-based pipeline, called CloudMap, which greatly simplifies the analysis of mutant genome sequences. Available on the Galaxy web platform, CloudMap requires no software installation when run on the cloud, but it can also be run locally or via Amazon's Elastic Compute Cloud (EC2) service. CloudMap uses a series of predefined workflows to pinpoint sequence variations in animal genomes, such as those of premutagenized and mutagenized Caenorhabditis elegans strains. In combination with a variant-based mapping procedure, CloudMap allows users to sharply define genetic map intervals graphically and to retrieve very short lists of candidate variants with a few simple clicks. Automated workflows and extensive video user guides are available to detail the individual analysis steps performed (http://usegalaxy.org/cloudmap). We demonstrate the utility of CloudMap for WGS analysis of C. elegans and Arabidopsis genomes and describe how other organisms (e.g., Zebrafish and Drosophila) can easily be accommodated by this software platform. To accommodate rapid analysis of many mutants from large-scale genetic screens, CloudMap contains an in silico complementation testing tool that allows users to rapidly identify instances where multiple alleles of the same gene are present in the mutant collection. Lastly, we describe the application of a novel mapping/WGS method ("Variant Discovery Mapping") that does not rely on a defined polymorphic mapping strain, and we integrate the application of this method into CloudMap. CloudMap tools and documentation are continually updated at http://usegalaxy.org/cloudmap.