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首页> 外文期刊>IEEE Transactions on Parallel and Distributed Systems >Improving Cache Performance for Large-Scale Photo Stores via Heuristic Prefetching Scheme
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Improving Cache Performance for Large-Scale Photo Stores via Heuristic Prefetching Scheme

机译：通过启发式预取方案提高大型照片商店的缓存性能

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摘要

Photo service providers are facing critical challenges of dealing with the huge amount of photo storage, typically in a magnitude of billions of photos, while ensuring national-wide or world-wide satisfactory user experiences. Distributed photo caching architecture is widely deployed to meet high performance expectations, where efficient still mysterious caching policies play essential roles. In this work, we present a comprehensive study on internet-scale photo caching algorithms in the case of QQPhoto from Tencent Inc., the largest social network service company in China. We unveil that even advanced cache algorithms can only perform at a similar level as simple baseline algorithms and there still exists a large performance gap between these cache algorithms and the theoretically optimal algorithm due to the complicated access behaviors in such a large multi-tenant environment. We then expound the reasons behind this phenomenon via extensively investigating the characteristics of QQPhoto workloads. Finally, in order to realistically further improve QQPhoto cache efficiency, we propose to incorporate a prefetcher in the cache stack based on the observed immediacy feature that is unique to the QQPhoto workload. The prefetcher proactively prefetches selected photos into cache before they are requested for the first time to eliminate compulsory misses and promote hit ratios. Our extensive evaluation results show that with appropriate prefetching we improve the cache hit ratio by up to 7.4 percent, while reducing the average access latency by 6.9 percent at a marginal cost of 4.14 percent backend network traffic compared to the original system that performs no prefetching.
机译：照片服务提供商在处理海量照片存储（通常是数十亿张照片）的同时，要确保全国范围或世界范围内令人满意的用户体验，面临着严峻的挑战。分布式照片缓存体系结构已被广泛部署以满足高性能的期望，高效而神秘的缓存策略在其中起着至关重要的作用。在这项工作中，我们以中国最大的社交网络服务公司腾讯公司的QQPhoto为例，对互联网规模的照片缓存算法进行了全面的研究。我们揭示，即使是高级的缓存算法也只能在与简单基准算法相似的水平上执行，并且由于在如此大的多租户环境中的复杂访问行为，这些缓存算法与理论上最优的算法之间仍然存在较大的性能差距。然后，我们通过广泛研究QQPhoto工作负载的特征来阐明此现象背后的原因。最后，为了切实地进一步提高QQPhoto缓存效率，我们建议根据观察到的QQPhoto工作负载特有的即时性，在缓存堆栈中合并预取器。预取器会在第一次请求之前将选定的照片主动预取到缓存中，以消除强制性遗漏并提高命中率。我们广泛的评估结果表明，与不进行预取的原始系统相比，通过适当的预取，我们可以将缓存命中率提高高达7.4％，同时将平均访问延迟降低6.9％，后端网络流量的边际成本为4.14％。

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