利用资源预测实现预期动态配置

V. Poladian, D. Garlan, M. Shaw, M. Satyanarayanan, B. Schmerl, J. Sousa
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引用次数: 65

摘要

基于多个并发应用程序的自适应系统必须决定如何将稀缺资源分配给应用程序,以及如何设置每个应用程序的质量参数以最好地满足用户。过去的工作是通过使用当前资源可用性信息的分析模型做出这些决策的:它们对资源可用性中最近发生的变化做出反应,而不是预测未来的可用性。这些反应性技术可能对每个局部决策进行最优建模,但随着时间的推移,决策的积累几乎总是变得不那么理想。在本文中,我们提出了一种自我适应的方法,称为预期配置,它利用对未来资源可用性的预测来提高用户在任务期间的效用。该方法解决了以下技术挑战:(1)如何表达资源可用性预测;(2)如何组合来自多个来源的预测;(3)如何在提高用户效用的同时持续利用预测。我们的实验表明,当某些适应操作成本较高时,预期配置比被动配置为用户提供了更好的效用,同时在资源需求方面具有可比性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging Resource Prediction for Anticipatory Dynamic Configuration
Self-adapting systems based on multiple concurrent applications must decide how to allocate scarce resources to applications and how to set the quality parameters of each application to best satisfy the user. Past work has made those decisions with analytic models that used current resource availability information: they react to recent changes in resource availability as they occur, rather than anticipating future availability. These reactive techniques may model each local decision optimally, but the accumulation of decisions over time nearly always becomes less than optimal. In this paper, we propose an approach to self- adaptation, called anticipatory configuration that leverages predictions of future resource availability to improve utility for the user over the duration of the task. The approach solves the following technical challenges: (1) how to express resource availability prediction, (2) how to combine prediction from multiple sources, and (3) how to leverage predictions continuously while improving utility to the user. Our experiments show that when certain adaptation operations are costly, anticipatory configuration provides better utility to the user than reactive configuration, while being comparable in resource demand.
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