GOAL: Supporting General and Dynamic Adaptation in Computing Systems

GOAL: Supporting General and Dynamic Adaptation in Computing Systems
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DOI:
10.1145/3563835.3567655
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发表时间:
2022-11
期刊:
Proceedings of the 2022 ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software
影响因子:
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通讯作者:
Ahsan Pervaiz;Yao-Hsiang Yang;Adam Duracz;F. Bartha;R. Sai;Connor Imes;Robert Cartwright;K. Palem;Shan Lu;Henry Hoffmann
Ahsan Pervaiz;Yao-Hsiang Yang;Adam Duracz;F. Bartha;R. Sai;Connor Imes;Robert Cartwright;K. Palem;Shan Lu;Henry Hoffmann
中科院分区:
其他
文献类型:
--
作者:
Ahsan Pervaiz;Yao-Hsiang Yang;Adam Duracz;F. Bartha;R. Sai;Connor Imes;Robert Cartwright;K. Palem;Shan Lu;Henry Hoffmann

文献摘要

相似文献

自适应计算系统会自动监测自身行为,并动态调整自身的配置参数(或旋钮),以确保在系统受到不可预测的外部干扰时仍能满足用户目标。先前的自适应框架的一个主要局限是,其内部自适应逻辑是为一组特定的、狭隘的目标和旋钮而实现的,这阻碍了复杂自适应系统的开发,因为这些系统在不同的部署中必须使用不同组的旋钮来满足不同的目标,甚至在一次部署过程中也可能改变目标。为克服这一局限,我们提出了GOAL,这是一种自适应框架,其独特之处在于它的虚拟化自适应逻辑是独立于任何特定目标或旋钮实现的。GOAL通过一个编程接口来支持这一逻辑,该接口允许用户在运行的程序中定义和操作各种各样的目标和旋钮。我们通过使用它重新实现文献中的七个不同的自适应系统来展示GOAL的优势,每个系统都有一组不同的目标和旋钮。我们表明GOAL的通用方法在实现目标方面与为特定目标和旋钮设计的先前方法一样好。在运行时目标和旋钮被修改的动态场景中,GOAL达到了93.7(此处似乎信息不完整)
Adaptive computing systems automatically monitor their behavior and dynamically adjust their own configuration parameters—or knobs—to ensure that user goals are met despite unpredictable external disturbances to the system. A major limitation of prior adaptation frameworks is that their internal adaptation logic is implemented for a specific, narrow set of goals and knobs, which impedes the development of complex adaptive systems that must meet different goals using different sets of knobs for different deployments, or even change goals during one deployment. To overcome this limitation we propose GOAL, an adaptation framework distinguished by its virtualized adaptation logic implemented independently of any specific goals or knobs. GOAL supports this logic with a programming interface allowing users to define and manipulate a wide range of goals and knobs within a running program. We demonstrate GOAL’s benefits by using it re-implement seven different adaptive systems from the literature, each of which has a different set of goals and knobs. We show GOAL’s general approach meets goals as well as prior approaches designed for specific goals and knobs. In dynamic scenarios where the goals and knobs are modified at runtime, GOAL achieves 93.7