An Automated Tool Profiling Service for the Cloud
An Automated Tool Profiling Service for the Cloud
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云自动化工具分析服务
DOI：
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发表时间：
2016
期刊：
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通讯作者：
Ian T Foster
中科院分区：
文献类型：
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作者：
Ryan Chard;K. Chard;Bryan K. F. Ng;K. Bubendorfer;Alex Rodriguez;Ravi K. Madduri;Ian T Foster
云提供商提供了一组具有不同资源容量的实例类型，旨在满足广泛的用户需求。虽然这种灵活性是云计算模型的主要优点，但在为给定应用程序选择最合适的实例类型时，它也带来了挑战。次优的实例选择可能导致性能低下和/或成本增加，在重复执行应用程序时会产生重大影响。然而，选择最佳实例类型是具有挑战性的，因为每个实例类型可以以不同的方式配置，应用程序性能取决于输入数据和配置，并且实例类型和应用程序经常更新。我们提出了一个服务，支持自动分析不同实例类型的应用程序性能，以创建丰富的应用程序配置文件，可用于比较，供应和调度。该服务可以动态配置云实例，自动部署和上下文化应用程序，传输输入数据集，监控执行性能，并创建具有细粒度资源使用信息的复合配置文件。我们使用来自四个生产基因组学网关的真实的使用数据，并估计在自主供应系统中使用配置文件可以减少高达15.7%的执行时间和高达86.6%的成本。
Cloud providers offer a diverse set of instance types with varying resource capacities, designed to meet the needs of a broad range of user requirements. While this flexibility is a major benefit of the cloud computing model, it also creates challenges when selecting the most suitable instance type for a given application. Sub-optimal instance selection can result in poor performance and/or increased cost, with significant impacts when applications are executed repeatedly. Yet selecting an optimal instance type is challenging, as each instance type can be configured differently, application performance is dependent on input data and configuration, and instance types and applications are frequently updated. We present a service that supports automatic profiling of application performance on different instance types to create rich application profiles that can be used for comparison, provisioning, and scheduling. This service can dynamically provision cloud instances, automatically deploy and contextualize applications, transfer input datasets, monitor execution performance, and create a composite profile with fine grained resource usage information. We use real usage data from four production genomics gateways and estimate the use of profiles in autonomic provisioning systems can decrease execution time by up to 15.7% and cost by up to 86.6%.