分段和基于流行度的缓存替换策略对命名数据网络的影响

R. Negara, Novan Purba Wasesa, Zaid Muhammad, Ratna Mayasari, Sri Astuti
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引用次数: 0

摘要

互联网协议(IP)技术的数据分发机制效率低下,因为它要求用户等待服务器的响应。 命名数据网络(NDN)是一项正在评估的用于增强 IP 网络的尖端技术,主要是因为它在每个路由器上都采用了数据包缓存技术。然而,这种方法的有效性在很大程度上取决于路由器的内容容量,因此需要在路由器容量满载时使用数据替换机制。 在缓存替换策略中采用了最近最少使用(LRU)法,但这种方法被认为是无效的,因为它忽略了内容的受欢迎程度。LRU 算法会替换不常请求的数据,当多个用户不断请求时,会导致热门数据的缓存效率低下。 为解决这一问题,我们提出了一种考虑内容流行度的分段 LRU(SLRU)替换策略。分段式 LRU 将评估流行内容和以前在两个分段类别(即试用分段和保护分段)中流行过的内容。 伊卡洛斯模拟器用于评估多个综合场景。 实验结果表明,与现有的缓存替换策略(如先进先出(FIFO)、LRU 和 Climb)相比,SLRU 能获得更好的缓存命中率(CHR),并能最大限度地减少延迟和链路负载。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of Segmentation and Popularity-based Cache Replacement Policies on Named Data Networking
The data distribution mechanism of internet protocol (IP) technology is inefficient because it necessitates the user to await a response from the server.  Named data networking (NDN) is a cutting-edge technology being assessed for enhancing IP networks, primarily because it incorporates a data packet caching technique on every router. However, the effectiveness of this approach is highly dependent on the router's content capacity, thus requiring the use data replacement mechanism when the router capacity is full.  The least recently used (LRU) method is employed for cache replacement policy; yet, it is considered ineffective as it neglects the content's popularity. The LRU algorithm replaces the infrequently requested data, leading to inefficient caching of popular data when multiple users constantly request it.  To address this problem, we propose a segmented LRU (SLRU) replacement strategy that considers content popularity. The SLRU will evaluate both popular content and content that has previously been popular in two segment categories, namely the probationary and protected segments.  Icarus simulator was used to evaluate multiple comprehensive scenarios.  Our experimental results show that the SLRU obtains a better cache hit ratio (CHR) and able to minimize latency and link load compared to existing cache replacement policies such as First In, First Out (FIFO), LRU, and Climb.
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