Distributed storage systems are known to be susceptible to long response time, and higher latency leads to a reduction in customers satisfaction. An elegant solution to reduce latency in such systems is through two methods - having redundancy in contents at the storage nodes, and adding a cache close to end-users. Redundancy could be added using an erasure code because of its high resiliency with low storage overhead. It is important to quantify the performance of distributed storage systems in the presence of redundancy and caching, which is the focus of this work. This paper proposes a framework for quantifying and jointly optimizing mean and tail latency in erasure-coded storage systems with edge-caching capabilities. A novel caching policy for caching contents in erasure-coded storage systems, called time-to-live (TTLCache), is proposed. Using TTLCache policy and probabilistic server-selection techniques, bounds for mean latency and latency tail probability (LTP) are characterized. A convex combination of both metrics is optimized over the choices of probabilistic scheduling and TTLCache parameters using an efficient algorithm. In all tested cases, the experimental results show the superiority of our approach as compared to the state of the other algorithms and some competitive baselines. Implementation in a real cloud environment is further used to validate the results.

中文翻译：

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TTLCache：通过 TTL 缓存控制纠删码存储中的延迟

众所周知，分布式存储系统容易受到较长响应时间的影响，而更高的延迟会导致客户满意度降低。减少此类系统中延迟的一种优雅解决方案是通过两种方法 - 在存储节点上具有内容冗余，并在靠近最终用户的位置添加缓存。可以使用纠删码添加冗余，因为它具有高弹性和低存储开销。在存在冗余和缓存的情况下量化分布式存储系统的性能很重要，这是本工作的重点。本文提出了一个框架，用于量化和联合优化具有边缘缓存功能的纠删码存储系统中的平均和尾部延迟。提出了一种新的缓存策略，用于在纠删码存储系统中缓存内容，称为生存时间 (TTLCache)。使用 TTLCache 策略和概率服务器选择技术，对平均延迟和延迟尾部概率 (LTP) 的界限进行了表征。两个指标的凸组合使用有效的算法在概率调度和 TTLCache 参数的选择上进行了优化。在所有测试案例中，实验结果表明，与其他算法和一些竞争基线的状态相比，我们的方法具有优越性。在真实云环境中的实施进一步用于验证结果。实验结果表明，与其他算法和一些竞争基线的状态相比，我们的方法具有优越性。在真实云环境中的实施进一步用于验证结果。实验结果表明，与其他算法和一些竞争基线的状态相比，我们的方法具有优越性。在真实云环境中的实施进一步用于验证结果。