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Recommending heterogeneous resources for science gateway applications based on custom templates composition

机译：为基于自定义模板组成的科学网关应用程序推荐异构资源

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摘要

Emerging interdisciplinary data-intensive science gateway applications in engineering fields (e.g., bioinformatics, cybermanufacturing) demand the use of high-performance computing resources. However, to mitigate operational costs and management efforts for these science gateway applications, there is a need to effectively deploy them on federated heterogeneous resources managed by external Cloud Service Providers (CSPs). In this paper, we present a novel methodology to deliver fast, automatic and flexible resource provisioning services for such application-owners with limited expertise in composing and deploying suitable cloud architectures. Our methodology features a Component Abstraction Model to implement intelligent resource 'abstractions' coupled with 'reusable' hardware and software configuration in the form of "custom templates" to simplify heterogeneous resource management efforts. It also features a novel middleware that provides services via a set of recommendation schemes for a context-aware requirement-collection questionnaire. Recommendations match the requirements to available resources and thus assist novice and expert users to make relevant configuration selections with CSP collaboration. To evaluate our middleware, we study the impact of user preferences in requirement collection, jobs execution and resource adaptation for a real-world manufacturing application on Amazon Web Services and the GENI cloud platforms. Our experiment results show that our scheme improves the resource recommendation accuracy in the manufacturing science gateway application by up to 21% compared to the existing schemes. We also show the impact of custom templates knowledgebase maturity at the CSP side for handling novice and expert user preferences in terms of the resource recommendation accuracy. (C) 2019 Elsevier B.V. All rights reserved.
机译：工程领域（例如，生物信息学，网络制造）中新兴的跨学科数据密集型科学网关应用程序要求使用高性能计算资源。但是，为了减轻这些科学网关应用程序的运营成本和管理工作，需要将它们有效地部署在由外部云服务提供商（CSP）管理的联合异构资源上。在本文中，我们提出了一种新颖的方法，可以为在组合和部署合适的云架构方面缺乏专门知识的此类应用程序所有者提供快速，自动和灵活的资源供应服务。我们的方法采用“组件抽象模型”，以“自定义模板”的形式实现智能资源“抽象”以及“可重用”硬件和软件配置，以简化异构资源管理工作。它还具有一种新颖的中间件，该中间件通过一套针对上下文感知的需求收集调查表的推荐方案来提供服务。建议将需求与可用资源相匹配，从而通过CSP协作帮助新手和专家用户进行相关配置选择。为了评估我们的中间件，我们研究了用户偏好对亚马逊Web服务和GENI云平台上的实际制造应用程序的需求收集，作业执行和资源适应的影响。我们的实验结果表明，与现有方案相比，该方案将制造科学网关应用程序中的资源推荐准确性提高了21％。我们还将展示自定义模板知识库成熟度在CSP方面对于处理新手和专家用户偏好方面的资源推荐准确性的影响。 （C）2019 Elsevier B.V.保留所有权利。

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