10 research outputs found
Dynamically Partitioning Workflow over Federated Clouds For Optimising the Monetary Cost and Handling Run-Time Failures
Several real-world problems in domain of healthcare, large scale scientific simulations, and manufacturing are organised as workflow applications. Efficiently managing workflow applications on the Cloud computing data-centres is challenging due to the following problems: (i) they need to perform computation over sensitive data (e.g. Healthcare workflows) hence leading to additional security and legal risks especially considering public cloud environments and (ii) the dynamism of the cloud environment can lead to several run-time problems such as data loss and abnormal termination of workflow task due to failures of computing, storage, and network services. To tackle above challenges, this paper proposes a novel workflow management framework call DoFCF (Deploy on Federated Cloud Framework) that can dynamically partition scientific workflows across federated cloud (public/private) data-centres for minimising the financial cost, adhering to security requirements, while gracefully handling run-time failures. The framework is validated in cloud simulation tool (CloudSim) as well as in a realistic workflow-based cloud platform (e-Science Central). The results showed that our approach is practical and is successful in meeting users security requirements and reduces overall cost, and dynamically adapts to the run-time failures
From Scripted HPC-Based NGS Pipelines to Workflows on the Cloud
In this paper we describe our initial experiences in the Cloud-e-Genome project with moving the whole exome sequencing pipeline from the scripted HPC-based solution to a workflow enactment system running in the cloud. We discuss shortcomings of the existing approach based on scripts and list benefits that a workflow-based solution can provide. Despite the effort it involved to wrap all required tools in the form of workflow blocks and the restrictions of the dataflow model used to represent workflows we expect the migration to significantly improve the current status of the pipeline. Our target is to enable flexibility, traceability and reproducibility of the solution, so that it can better fit the evolution of tools, data and pipeline itself and allow us to run it at national scale. This work will become foundation for the more complete system that includes variant filtering and interpretation for the diagnostic purposes
From scripted HPC-based NGS pipelines to workflows on the cloud
In this paper we describe our initial experiences in the Cloud-e-Genome project with moving the whole exome sequencing pipeline from the scripted HPC-based solution to a workflow enactment system running in the cloud. We discuss shortcomings of the existing approach based on scripts and list benefits that a workflow-based solution can provide. Despite the effort it involved to wrap all required tools in the form of workflow blocks and the restrictions of the dataflow model used to represent workflows we expect the migration to significantly improve the current status of the pipeline. Our target is to enable flexibility, traceability and reproducibility of the solution, so that it can better fit the evolution of tools, data and pipeline itself and allow us to run it at national scale. This work will become foundation for the more complete system that includes variant filtering and interpretation for the diagnostic purposes.</p
From scripted HPC-based NGS pipelines to workflows on the cloud
In this paper we describe our initial experiences in the Cloud-e-Genome project with moving the whole exome sequencing pipeline from the scripted HPC-based solution to a workflow enactment system running in the cloud. We discuss shortcomings of the existing approach based on scripts and list benefits that a workflow-based solution can provide. Despite the effort it involved to wrap all required tools in the form of workflow blocks and the restrictions of the dataflow model used to represent workflows we expect the migration to significantly improve the current status of the pipeline. Our target is to enable flexibility, traceability and reproducibility of the solution, so that it can better fit the evolution of tools, data and pipeline itself and allow us to run it at national scale. This work will become foundation for the more complete system that includes variant filtering and interpretation for the diagnostic purposes.</p
Reprodutibilidade e reuso de experimentos em eScience : workflows, ontologias e scripts
Orientadores: Claudia Maria Bauzer Medeiros, Yolanda GilTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Scripts e Sistemas Gerenciadores de Workflows Científicos (SGWfC) são abordagens comumente utilizadas para automatizar o fluxo de processos e análise de dados em experimentos científicos computacionais. Apesar de amplamente usados em diversas disciplinas, scripts são difíceis de entender, adaptar, reusar e reproduzir. Por esta razão, diversas soluções têm sido propostas para auxiliar na reprodutibilidade de experimentos que utilizam ambientes baseados em scripts. Porém, estas soluções não permitem a documentação completa do experimento, nem ajudam quando outros cientistas querem reusar apenas parte do código do script. SGWfCs, por outro lado, ajudam na documentação e reuso através do suporte aos cientistas durante a modelagem e execução dos seus experimentos, que são especificados e executados como componentes interconectados (reutilizáveis) de workflows. Enquanto workflows são melhores que scripts para entendimento e reuso dos experimentos, eles também exigem documentação adicional. Durante a modelagem de um experimento, cientistas frequentemente criam variantes de workflows, e.g., mudando componentes do workflow. Reuso e reprodutibilidade exigem o entendimento e rastreamento da proveniência das variantes, uma tarefa que consome muito tempo. Esta tese tem como objetivo auxiliar na reprodutibilidade e reuso de experimentos computacionais. Para superar estes desafios, nós lidamos com dois problemas de pesquisas: (1) entendimento de um experimento computacional, e (2) extensão de um experimento computacional. Nosso trabalho para resolver estes problemas nos direcionou na escolha de workflows e ontologias como respostas para ambos os problemas. As principais contribuições desta tese são: (i) apresentar os requisitos para a conversão de experimentos baseados em scripts em experimentos reprodutíveis; (ii) propor uma metodologia que guia o cientista durante o processo de conversão de experimentos baseados em scripts em workflow research objects reprodutíveis. (iii) projetar e implementar funcionalidades para avaliação da qualidade de experimentos computacionais; (iv) projetar e implementar o W2Share, um arcabouço para auxiliar a metodologia de conversão, que explora ferramentas e padrões que foram desenvolvidos pela comunidade científica para promover o reuso e reprodutibilidade; (v) projetar e implementar o OntoSoft-VFF, um arcabouço para captura de informação sobre software e componentes de workflow para auxiliar cientistas a gerenciarem a exploração e evolução de workflows. Nosso trabalho é apresentado via casos de uso em Dinâmica Molecular, Bioinformática e Previsão do TempoAbstract: Scripts and Scientific Workflow Management Systems (SWfMSs) are common approaches that have been used to automate the execution flow of processes and data analysis in scientific (computational) experiments. Although widely used in many disciplines, scripts are hard to understand, adapt, reuse, and reproduce. For this reason, several solutions have been proposed to aid experiment reproducibility for script-based environments. However, they neither allow to fully document the experiment nor do they help when third parties want to reuse just part of the code. SWfMSs, on the other hand, help documentation and reuse by supporting scientists in the design and execution of their experiments, which are specified and run as interconnected (reusable) workflow components (a.k.a. building blocks). While workflows are better than scripts for understandability and reuse, they still require additional documentation. During experiment design, scientists frequently create workflow variants, e.g., by changing workflow components. Reuse and reproducibility require understanding and tracking variant provenance, a time-consuming task. This thesis aims to support reproducibility and reuse of computational experiments. To meet these challenges, we address two research problems: (1) understanding a computational experiment, and (2) extending a computational experiment. Our work towards solving these problems led us to choose workflows and ontologies to answer both problems. The main contributions of this thesis are thus: (i) to present the requirements for the conversion of script to reproducible research; (ii) to propose a methodology that guides the scientists through the process of conversion of script-based experiments into reproducible workflow research objects; (iii) to design and implement features for quality assessment of computational experiments; (iv) to design and implement W2Share, a framework to support the conversion methodology, which exploits tools and standards that have been developed by the scientific community to promote reuse and reproducibility; (v) to design and implement OntoSoft-VFF, a framework for capturing information about software and workflow components to support scientists manage workflow exploration and evolution. Our work is showcased via use cases in Molecular Dynamics, Bioinformatics and Weather ForecastingDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2013/08293-7, 2014/23861-4, 2017/03570-3FAPES
Partitioning workflow applications over federated clouds to meet non-functional requirements
PhD ThesisWith cloud computing, users can acquire computer resources when they need them
on a pay-as-you-go business model. Because of this, many applications are now being
deployed in the cloud, and there are many di erent cloud providers worldwide. Importantly,
all these various infrastructure providers o er services with di erent levels
of quality. For example, cloud data centres are governed by the privacy and security
policies of the country where the centre is located, while many organisations have
created their own internal \private cloud" to meet security needs.
With all this varieties and uncertainties, application developers who decide to host their
system in the cloud face the issue of which cloud to choose to get the best operational
conditions in terms of price, reliability and security. And the decision becomes even
more complicated if their application consists of a number of distributed components,
each with slightly di erent requirements.
Rather than trying to identify the single best cloud for an application, this thesis
considers an alternative approach, that is, combining di erent clouds to meet users'
non-functional requirements. Cloud federation o ers the ability to distribute a single
application across two or more clouds, so that the application can bene t from the
advantages of each one of them. The key challenge for this approach is how to nd the
distribution (or deployment) of application components, which can yield the greatest
bene ts. In this thesis, we tackle this problem and propose a set of algorithms, and a
framework, to partition a work
ow-based application over federated clouds in order to
exploit the strengths of each cloud. The speci c goal is to split a distributed application
structured as a work
ow such that the security and reliability requirements of each
component are met, whilst the overall cost of execution is minimised.
To achieve this, we propose and evaluate a cloud broker for partitioning a work
ow
application over federated clouds. The broker integrates with the e-Science Central
cloud platform to automatically deploy a work
ow over public and private clouds.
We developed a deployment planning algorithm to partition a large work
ow appli-
- i -
cation across federated clouds so as to meet security requirements and minimise the
monetary cost.
A more generic framework is then proposed to model, quantify and guide the partitioning
and deployment of work
ows over federated clouds. This framework considers
the situation where changes in cloud availability (including cloud failure) arise during
work
ow execution
Simulation of the performance of complex data-intensive workflows
PhD ThesisRecently, cloud computing has been used for analytical and data-intensive processes
as it offers many attractive features, including resource pooling, on-demand capability
and rapid elasticity. Scientific workflows use these features to tackle the problems of
complex data-intensive applications. Data-intensive workflows are composed of many
tasks that may involve large input data sets and produce large amounts of data as
output, which typically runs in highly dynamic environments. However, the resources
should be allocated dynamically depending on the demand changes of the work
flow, as over-provisioning increases the cost and under-provisioning causes Service Level
Agreement (SLA) violation and poor Quality of Service (QoS). Performance prediction
of complex workflows is a necessary step prior to the deployment of the workflow.
Performance analysis of complex data-intensive workflows is a challenging task due
to the complexity of their structure, diversity of big data, and data dependencies, in
addition to the required examination to the performance and challenges associated
with running their workflows in the real cloud.
In this thesis, a solution is explored to address these challenges, using a Next Generation
Sequencing (NGS) workflow pipeline as a case study, which may require hundreds/
thousands of CPU hours to process a terabyte of data. We propose a methodology to
model, simulate and predict runtime and the number of resources used by the complex
data-intensive workflows. One contribution of our simulation methodology is that it
provides an ability to extract the simulation parameters (e.g., MIPs and BW values)
that are required for constructing a training set and a fairly accurate prediction of
the run time for input for cluster sizes much larger than ones used in training of the
prediction model. The proposed methodology permits the derivation of run time prediction
based on historical data from the provenance fi les. We present the run time
prediction of the complex workflow by considering different cases of its running in the
cloud such as execution failure and library deployment time. In case of failure, the
framework can apply the prediction only partially considering the successful parts of
the pipeline, in the other case the framework can predict with or without considering
the time to deploy libraries. To further improve the accuracy of prediction, we propose
a simulation model that handles I/O contention