Automatic configuration and run-time adaptation of distributed applications

Fangzhe Chang, V. Karamcheti
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引用次数: 76

Abstract

Increased platform heterogeneity and varying resource availability in distributed systems motivates the design of resource-aware applications, which ensure a desired performance level by continuously adapting their behavior to changing resource characteristics. In this paper, we describe an application-independent adaptation framework that simplifies the design of resource-aware applications. This framework eliminates the need for adaptation decisions to be explicitly programmed into the application by relying on two novel components: (1) a tunability interface, which exposes adaptation choices in the form of alternate application configurations while encapsulating core application functionality, and (2) a virtual execution environment, which emulates application execution under diverse resource availability enabling off-line collection of information about the resulting behavior. Together, these components permit automatic run-time decisions on when to adapt by continuously monitoring resource conditions and application progress, and how to adapt by dynamically choosing the application configuration that is most appropriate for the prescribed user preference. We evaluate the framework using an interactive distributed image visualization application. The framework permits automatic adaptation to changes in CPU load and network bandwidth by choosing a different compression algorithm or by controlling the image transmission sequence so as to satisfy user preferences of visualization quality and timeliness.
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分布式应用程序的自动配置和运行时适应
分布式系统中不断增加的平台异构性和不断变化的资源可用性激发了资源感知应用程序的设计,这些应用程序通过不断调整其行为以适应不断变化的资源特征来确保所需的性能水平。在本文中,我们描述了一个独立于应用程序的自适应框架,它简化了资源感知应用程序的设计。该框架通过依赖于两个新的组件,消除了将适应决策显式地编程到应用程序中的需要:(1)可调接口,它在封装核心应用程序功能的同时,以替代应用程序配置的形式公开适应选择;(2)虚拟执行环境,它模拟不同资源可用性下的应用程序执行,支持有关结果行为的离线信息收集。总之,这些组件允许通过持续监视资源条件和应用程序进度来自动决定何时进行调整,以及如何通过动态选择最适合指定用户首选项的应用程序配置来进行调整。我们使用交互式分布式图像可视化应用程序来评估该框架。该框架可以通过选择不同的压缩算法或控制图像传输顺序来自动适应CPU负载和网络带宽的变化,从而满足用户对可视化质量和时效性的偏好。
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