Multi-resource fair allocation for consolidated flash-based caching systems
Multi-resource fair allocation for consolidated flash-based caching systems
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DOI：
10.1145/3528535.3565245
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发表时间：
2022-11
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影响因子：
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通讯作者：
Wonil Choi;B. Urgaonkar;M. Kandemir;G. Kesidis
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作者：
Wonil Choi;B. Urgaonkar;M. Kandemir;G. Kesidis
使用基于闪存的层来满足多个工作负载的缓存和缓冲需求已成为一种常见做法。在这种情况下，资源需求有时将不可避免地超过可用能力。“公平”资源分配可以提供在这种稀缺时期跨竞争工作负载划分资源的系统方式。现有的作品只提供公平的分配策略，为一个单一的资源（容量或带宽）内的闪存设备隔离。然而，由于在闪存设备内存在需要被划分的多个关键资源并且它们彼此相关，因此单个资源的公平分配可能导致其他资源的浪费或工作负载的性能降级。为此，我们提出了一个多资源公平分配解决方案的情况下，基于闪存的缓存，整合多个工作负载。此外，我们认为，设备的生命周期，这取决于运行的工作负载的行为，也应该被认为是一个一流的资源，与容量和带宽。具体而言，我们建立在现有的想法有关的主导资源公平性（DRF），设计闪存特定的多资源公平算法：（i）nDRF，联合分配容量和带宽，考虑到他们的非线性关系;（ii）nDRF，明确考虑寿命以及在其分配;和（iii）这些的几个变种。我们的实验评估提供了重要的发现：（i）与隔离分区容量的最新技术相比，nDRF和CMDRF都导致上级性能公平性;（ii）CMDRF还提供了改进的设备“磨损”行为;以及（iii）我们的算法结合合理的需求预测，在具有工作负载动态性和不确定性的在线设置中工作得非常好。
Using a flash-based layer to serve the caching and buffering needs of multiple workloads has become a common practice. In such settings, resource demands will inevitably exceed available capacity sometimes. "Fair" resource allocation may offer a systematic way of partitioning resources across competing workloads during such periods of scarcity. Existing works only offer fair allocation strategies for a single resource (capacity or bandwidth) within a flash device in isolation. However, since there exist multiple critical resources that need to be partitioned within a flash device and they are correlated to each other, fair allocation of a single resource may result in a waste of other resource(s) or performance degradation of workload(s). To this end, we make a case for multi-resource fair allocation solutions for flash-based caches that consolidate multiple workloads. Furthermore, we argue that device lifetime, which depends on the behavior of running workloads, should also be considered as a first-class resource on par with capacity and bandwidth. Specifically, we build upon existing ideas related to dominant resource fairness (DRF) to devise flash-specific multi-resource fair algorithms: (i) nDRF, that jointly allocates capacity and bandwidth taking their non-linear relationship into account; (ii) ℓDRF, that explicitly considers lifetime as well in its allocation; and (iii) several variants of these. Our experimental evaluation offers important findings: (i) both nDRF and ℓDRF result in superior performance fairness compared to the state-of-the-art techniques that partition capacity in isolation; (ii) ℓDRF additionally offers improved device "wear" behavior; and (iii) our algorithms combined with reasonable demand prediction work very well in online settings with workload dynamism and uncertainty.