优化雾计算边缘服务器上的存储:最近的一种算法设计,干扰最小
Xumin Zhao1,2,3, Guojie Xie2, Yi Luo1,2
1Zhejiang Yuexiu University, Shaoxing, China.
PloS one
|July 10, 2024
概括
一个新的算法,最近使用的低干扰 (LIRU),优化了雾计算中的边缘服务器存储. LIRU提高了数据访问效率,减少了延迟,提高了整体系统性能.
科学领域:
- 计算机科学 计算机科学
- 分布式系统 分布式系统
- 云计算 云计算 云计算 云计算
背景情况:
- 雾计算利用边缘服务器进行分布式数据处理和存储.
- 优化边缘服务器上的存储容量对于雾计算效率至关重要.
- 像LRU和LFU这样的现有算法在受约束的存储环境中存在局限性.
研究的目的:
- 为雾计算边缘服务器开发一种新的存储优化算法.
- 为了提高存储利用率,提高数据访问效率,并减少访问延迟.
- 为应对雾基础设施中有限的存储资源所带来的挑战.
主要方法:
- 开发了最近使用的低干扰 (LIRU) 算法,结合了LIRS和LRU原则.
- 分析边缘服务器存储资源,重点关注利用率和访问频率.
- 构建了一个优化模型,将数据频率与缓存容量协调一致.
主要成果:
- 与LRU和LFU等传统算法相比,LIRU表现出了优越的性能.
- 与LRU相比,击中率提高了66%,与LFU相比增加了5%.
- 与LRU和LFU相比,平均系统响应时间缩短了高达16.5%.
结论:
- 在雾计算中,LIRU算法显著提高了存储利用率和数据访问效率.
- 这项研究为雾计算架构中的数据管理提供了有价值的框架.
- 这些发现有助于提高雾计算生态系统的性能和效率.
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