生命科学网格中服务水平协议的资源质量评估

Tibor K´lm´n
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摘要

本文的重点是测量、描述、监控和发布网格资源的质量和性能。生命科学社区可以与其资源提供者使用服务水平协议(sla)来确保服务的交付。为此,对于生命科学社区和他们的提供者来说,理解和量化不同网格环境的性能和服务质量是很重要的。然而,在使用不同中间件的网格基础设施中测量服务质量(如在德国网格计划中)是一个复杂的问题。我们描述了德国生命科学社区MediGRID, Services@MediGRID和pneumgrid目前使用的质量指标的状态。我们还确定了用于定义和监控D-Grid中网格资源质量的进一步质量指标。网格信息系统是网格服务的入口,通过网格信息系统发布和交换高质量的信息非常重要。因此,我们还介绍了GLUE v2.0 Schema如何处理高质量的信息,这是网格信息系统即将使用的标准数据模型。为了在多中间件环境中测量和监视质量度量,讨论了两种方法。第一种方法是从外部基准系统中提取质量信息,并将其加载到网格信息系统中。第二个解决方案针对的是不使用遗留基准测试系统,而是操作传统监视系统(如Nagios)的生命科学社区。
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Assessment of Resource Quality for Service Level Agreements in Life Science Grids
This article focuses on measuring, describing, monitoring and publishing the quality and performance of grid resources. Life science communities can employ Service Level Agreements (SLAs) with their resource providers to ensure the delivery of services. For this, it is important for both the life science communities and their providers to understand and quantify the performance and service quality of different grid environments. However, measuring service quality in grid infrastructures utilizing different middle wares, as in the German Grid Initiative, is a complex problem. We describe the state of quality metrics which are currently used by the German life science communities MediGRID, Services@MediGRID and PneumoGrid. We also identify further quality metrics for defining and monitoring grid resource quality in D-Grid. It is important to publish and exchange the quality information by grid information systems, which are the entry points to grid services. Therefore, we also present how quality information can be handled by the GLUE v2.0 Schema, which is the upcoming standard data model used by grid information systems. For measuring and monitoring the quality metrics in multi-middleware environments two approaches are discussed. The first approach extracts quality information from an external benchmarking system and loads iit to the grid information systems. The second solution targets life science communities that do not utilize legacy benchmarking systems, but operate traditional monitoring systems, like Nagios.
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