高效分布式上下文管理系统的自适应上下文缓存

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Shakthi Weerasinghe, A. Zaslavsky, S. Loke, A. Abken, A. Hassani, A. Medvedev
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引用次数: 5

摘要

我们认为性能指标驱动的自适应上下文缓存对分布式上下文管理系统(CMS)的性能效率有深远的影响。本文提出了一种自适应上下文缓存方法,该方法基于(i)缓存特定项目的经济学启发的预期回报模型,以及(ii)从历史上下文缓存性能中学习,即,我们的方法自适应地(相对于历史性能的统计数据)缓存“上下文”,目的是将CMS响应上下文查询所产生的成本降至最低。我们的新算法使上下文查询和子查询能够以一种有效的方式重用和重新利用缓存的上下文,这与传统的数据缓存不同。本文还提出了启发式和自适应策略,如驱逐和上下文缓存内存缩放。该方法使用受真实场景启发的综合生成的子查询负载进行评估。我们进一步研究了不同设置下的最佳自适应缓存配置。本文介绍并讨论了我们的研究结果,即所提出的统计选择性缓存方法在大量易变查询下快速达到短期成本最优。该方法的成本效率比相关算法高出47.9%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive Context Caching for Efficient Distributed Context Management Systems
We contend that performance metrics-driven adaptive context caching has a profound impact on performance efficiency in distributed context management systems (CMS). This paper proposes an adaptive context caching approach based on (i) a model of economics-inspired expected returns of caching particular items, and (ii) learning from historical context caching performance, i.e., our approach adaptively (with respect to statistics on historical performance) caches "context" with the objective of minimizing the cost incurred by a CMS in responding to context queries. Our novel algorithm enables context queries and sub-queries to reuse and repurpose cached context in an efficient manner, different from traditional data caching. The paper also proposes heuristics and adaptive policies such as eviction and context cache memory scaling. The method is evaluated using a synthetically generated load of sub-queries inspired by a real-world scenario. We further investigate optimal adaptive caching configurations under different settings. This paper presents and discusses our findings that the proposed statistical selective caching method reaches short-term cost optimality fast under massively volatile queries. The proposed method outperforms related algorithms by up to 47.9% in cost efficiency.
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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