Reducing the long tail of the query latency distribution in modern warehouse scale computers is critical for improving performance and quality of service (QoS) of workloads such as Web Search and Memcached. Traditional turbo boost increases a processor’s voltage and frequency during a coarse-grained sliding window, boosting all queries that are processed during that window. However, the inability of such a technique to pinpoint tail queries for boosting limits its tail reduction benefit. In this work, we propose Adrenaline , an approach to leverage finer-granularity (tens of nanoseconds) voltage boosting to effectively rein in the tail latency with query-level precision. Two key insights underlie this work. First, emerging finer granularity voltage/frequency boosting is an enabling mechanism for intelligent allocation of the power budget to precisely boost only the queries that contribute to the tail latency; second, per-query characteristics can be used to design indicators for proactively pinpointing these queries, triggering boosting accordingly. Based on these insights, Adrenaline effectively pinpoints and boosts queries that are likely to increase the tail distribution and can reap more benefit from the voltage/frequency boost. By evaluating under various workload configurations, we demonstrate the effectiveness of our methodology. We achieve up to a 2.50 × tail latency improvement for Memcached and up to a 3.03 × for Web Search over coarse-grained dynamic voltage and frequency scaling (DVFS) given a fixed boosting power budget. When optimizing for energy reduction, Adrenaline achieves up to a 1.81 × improvement for Memcached and up to a 1.99 × for Web Search over coarse-grained DVFS. By using the carefully chosen boost thresholds, Adrenaline further improves the tail latency reduction to 4.82 × over coarse-grained DVFS.

中文翻译：

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使用肾上腺素快速升压控制仓库规模计算机的长尾问题

减少现代仓库规模计算机中查询延迟分布的长尾对于提高 Web 搜索和 Memcached 等工作负载的性能和服务质量 (QoS) 至关重要。传统的 turbo boost 会在粗粒度滑动窗口期间增加处理器的电压和频率，从而提升在该窗口期间处理的所有查询。然而，这种技术无法精确定位尾部查询以进行提升限制了它的尾部减少优势。在这项工作中，我们建议肾上腺素，一种利用更细粒度（数十纳秒）电压提升的方法，以查询级别的精度有效地控制尾部延迟。这项工作有两个关键见解。首先，新兴的更细粒度的电压/频率提升是一种智能分配功率预算的启用机制，以精确地提升导致尾部延迟的查询；其次，可以使用每个查询的特征来设计指标，以主动查明这些查询，从而触发相应的提升。基于这些见解，Adrenaline 有效地查明和提升可能增加尾部分布的查询，并可以从电压/频率提升中获得更多收益。通过在各种工作负载配置下进行评估，我们证明了我们方法的有效性。我们达到了 2。给定固定的提升功率预算，在粗粒度动态电压和频率缩放 (DVFS) 上，Memcached 的尾部延迟提高了 50 倍，Web 搜索的尾部延迟提高了 3.03 倍。在优化能源减少时，Adrenaline 对 Memcached 的改进高达 1.81 倍，对于粗粒度 DVFS 的 Web 搜索的改进高达 1.99 倍。通过使用精心选择的提升阈值，Adrenaline 进一步将尾部延迟降低到 4.82 × 粗粒度 DVFS。