Fair Write Attribution and Allocation for Consolidated Flash Cache
Fair Write Attribution and Allocation for Consolidated Flash Cache
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DOI：
10.1145/3373376.3378502
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发表时间：
2020-03
期刊：
影响因子：
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通讯作者：
Wonil Choi;B. Urgaonkar;M. Kandemir;Myoungsoo Jung;D. Evans
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文献类型：
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作者：
Wonil Choi;B. Urgaonkar;M. Kandemir;Myoungsoo Jung;D. Evans
在单个基于闪存的存储设备上整合多个工作负载现在已成为一种常见做法。我们确定了一个新的问题，在这样的设置与生命周期管理：一个分区设备资源之间的整合工作负载，使其允许的贡献，设备的磨损（导致他们的写，包括隐藏的写，由于垃圾收集）可能会被认为是公平分配？当闪存用作缓存/缓冲器时，这种公平性很重要，因为它影响来自各种工作负载的哪些流量和多少流量可以使用闪存来服务，这反过来又影响它们的性能。我们首先澄清为什么写归属问题（即，哪个工作负载贡献了多少写入）是重要的。然后，我们提出了一种技术，它的灵感来自Shapley值，合作博弈论的经典概念，并证明它是准确的，公平的，可行的。接下来我们考虑如何处理总体“写入预算”（即，给定时间段内允许的总写入量），将设备作为值得显式管理的一流资源。为此，我们提出了一种新的写预算分配技术。最后，我们构建了一个动态的生命周期管理框架，整合设备通过把上述元素在一起。我们使用真实世界的工作负载的实验表明，我们的写分配和归因技术导致跨整合的工作负载的性能公平性。
Consolidating multiple workloads on a single flash-based storage device is now a common practice. We identify a new problem related to lifetime management in such settings: how should one partition device resources among consolidated workloads such that their allowed contributions to the device's wear (resulting from their writes including hidden writes due to garbage collection) may be deemed fairly assigned? When flash is used as a cache/buffer, such fairness is important because it impacts what and how much traffic from various workloads may be serviced using flash which in turn affects their performance. We first clarify why the write attribution problem (i.e., which workload contributed how many writes) is non-trivial. We then present a technique for it inspired by the Shapley value, a classical concept from cooperative game theory, and demonstrate that it is accurate, fair, and feasible. We next consider how to treat an overall "write budget" (i.e., total allowable writes during a given time period) for the device as a first-class resource worthy of explicit management. Towards this, we propose a novel write budget allocation technique. Finally, we construct a dynamic lifetime management framework for consolidated devices by putting the above elements together. Our experiments using real-world workloads demonstrate that our write allocation and attribution techniques lead to performance fairness across consolidated workloads.