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Performance Evaluation for New Web Caching Strategies Combining LRU with Score Based Object Selection

机译：LRU与基于得分的对象选择相结合的新Web缓存策略的性能评估

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The topic of Internet content caching regained relevance over the last years due to the extended use of data center infrastructures in CDNs, clouds and ISP networks to meet the capacity and delay demands of multimedia services. In this study, we evaluate the performance of web caching strategies in terms of the achievable hit rate for realistic scenarios of large user populations. We focus on a class of score gated least recently used (SG-LRU) strategies which combine the simple update effort of the LRU policy with the flexibility to keep the most important content in the cache according to a predefined score function. Caching efficiency is evaluated via simulations assuming Zipf request pattern, which have been confirmed manifold in the access to popular web platforms for video streaming and other types of content. We analyze the possible hit rate gain of alternative web caching strategies over pure LRU for the standard independent request model (IRM) within the complete relevant range of the three basic system parameters. The results confirm that absolute hit rate gains of 10%-20% over LRU as observed in case studies for special caching strategies are a realistic estimation in general. Moreover, we compare IRM evaluations with results for dynamic request pattern over time using Wikipedia statistics, which recently have been made available as daily top-1000 page requests. Simulations are extended to show the impact of varying object popularity on the caching efficiency and to adapt a score-based caching strategy to increasing popularity dynamics.
机译：由于在CDN，云和ISP网络中数据中心基础架构的广泛使用，以满足多媒体服务的容量和延迟需求，Internet内容缓存这一主题在过去几年中重新获得了关注。在这项研究中，我们针对大型用户群体的实际情况，根据可达到的命中率评估了Web缓存策略的性能。我们专注于一类评分门控最近最少使用（SG-LRU）策略，该策略结合了LRU策略的简单更新工作以及根据预定义的评分功能将最重要的内容保留在缓存中的灵活性。通过假设Zipf请求模式的模拟来评估缓存效率，这已在访问流行的Web平台进行视频流和其他类型的内容访问时得到了证实。我们分析了在三个基本系统参数的完整相关范围内，对于标准独立请求模型（IRM），在纯LRU上替代Web缓存策略可能带来的命中率提高。结果证实，在特殊缓存策略的案例研究中观察到，与LRU相比，绝对命中率提高了10％-20％，这通常是一个现实的估计。此外，我们使用Wikipedia统计信息将IRM评估与动态请求模式随时间变化的结果进行比较，该统计信息最近已作为每日前1000名页面请求提供。扩展了仿真，以显示变化的对象流行度对缓存效率的影响，并使基于分数的缓存策略适应不断增加的流行度动态。

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