适应式上下文缓存用于基于物联网的应用程序:一种强化学习方法
Shakthi Weerasinghe1, Arkady Zaslavsky1, Seng Wai Loke1
1School of Information Technology, Deakin University, Geelong, VIC 3145, Australia.
Sensors (Basel, Switzerland)
|July 11, 2023
概括
适应式上下文缓存 (ACOCA) 通过提高上下文管理平台 (CMP) 的性能和成本效益来优化物联网 (IoT) 应用程序. 这种新的机制增强了实时的上下文查询响应,提供了显著的长期收益.
科学领域:
- 计算机科学 计算机科学
- 数据管理数据管理
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 应用程序需要广泛的数据处理来实现上下文意识.
- 在缓存中管理短暂的上下文数据是一个尚未探索的研究领域.
- 文本管理平台 (CMP) 在实时性能和成本效率方面面临挑战.
研究的目的:
- 为CMPs提出一种新的自适应上下文缓存 (ACOCA) 机制.
- 为了最大限度地提高成本和性能效率,近乎实时的上下文查询响应.
- 解决缓存和成本管理的高效上下文选择方面的挑战.
主要方法:
- 使用双延迟深度决定性政策梯度 (TD3) 方法实施可扩展的,选择性的上下文缓存代理.
- 纳入适应性上下文更新切换政策.
- 整合时间意识的驱逐政策和隐藏缓存决策管理政策.
主要成果:
- 在ACOCA的研究中,CMP的长期效率显著提高,超过了之前的研究.
- 该机制通过大幅增加成本和性能来证明其增加的复杂性.
- 根据真实世界的停车交通数据集进行评估,ACOCA在成本效益方面在传统和上下文意识的缓存政策中表现出色,高达84.7%.
结论:
- 阿科卡为整个上下文管理生命周期提供了全面的解决方案.
- 拟议的机制显著提高了CMP成本和绩效效率.
- ACOCA代表了物联网应用程序上下文感知缓存的实质性进步.
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