C ENSUS : Counting Interleaved Workloads on Shared Storage
C ENSUS : Counting Interleaved Workloads on Shared Storage
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发表时间：
2020
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通讯作者：
Si Chen;Jianqiao Liu;Avani Wildani
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作者：
Si Chen;Jianqiao Liu;Avani Wildani
- 了解影响存储系统延迟或可靠性的不同工作负载相关因素对于SLA满意度和公平资源调配至关重要。然而，由于多个工作负载下的系统行为的波动性，确定甚至并发类型的工作负载功能的数量，工作负载分离的必要前提，在一般情况下是一个未解决的问题。我们引入了C ENSUS，这是一种新颖的分类框架，它将时间序列分析与梯度提升相结合，通过将工作负载跟踪投影到高维特征表示空间中来识别共享存储系统中的功能工作负载数量。我们表明，C ENSUS可以区分交错工作负载的数量在现实世界中的跟踪段高达95%的准确性，导致减少的均方误差到5%相比，最公平的猜测，根据每日平均值。
—Understanding the different workload-dependent factors that impact the latency or reliability of a storage system is essential for SLA satisfaction and fair resource provisioning. However, due to the volatility of system behavior under multiple workloads, determining even the number of concurrent types of workload functions, a necessary precursor to workload separation, is an unsolved problem in the general case. We introduce C ENSUS , a novel classification framework that combines time-series analysis with gradient boosting to identify the number of functional workloads in a shared storage system by projecting workload traces into a high-dimensional feature representation space. We show that C ENSUS can distinguish the number of interleaved workloads in a real-world trace segment with up to 95% accuracy, leading to a decrement of the mean square error to as little as 5% compared to the fairest guess according to the daily average.