Contention-Aware Performance Prediction For Virtualized Network Functions
Contention-Aware Performance Prediction For Virtualized Network Functions
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DOI：
10.1145/3387514.3405868
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发表时间：
2020-07
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影响因子：
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通讯作者：
Antonis Manousis;R. Sharma;Vyas Sekar;Justine Sherry
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作者：
Antonis Manousis;R. Sharma;Vyas Sekar;Justine Sherry
网络功能虚拟化的核心是网络功能（NFs），它们共同驻留在同一台服务器上运行，争夺其硬件资源，因此，相对于在同一硬件上单独运行，它们的性能可能会降低。因此，为了有效地管理资源和满足性能sla， NFV编排器需要预测争用导致的性能下降的机制。在这项工作中，我们发现先前的性能预测框架在现代架构和NFs上的准确性很差，因为它们将内存视为一个整体。此外，我们还表明，在实际应用中，存在多个内存子系统组件，它们可以单独引发争用。通过精确描述(1)每个NF对服务器共享硬件资源施加的压力（争议性）和(2)每个NF对由于竞争争议性（敏感性）而导致的性能下降的易感性，我们开发了SLOMO，一个网络功能的多变量性能预测框架。我们表明，相对于之前的工作，SLOMO将预测误差降低了2-5倍，并使集群利用率提高了6-14%。SLOMO的代码库可以在https://github.com/cmu-snap/SLOMO上找到。
At the core of Network Functions Virtualization lie Network Functions (NFs) that run co-resident on the same server, contend over its hardware resources and, thus, might suffer from reduced performance relative to running alone on the same hardware. Therefore, to efficiently manage resources and meet performance SLAs, NFV orchestrators need mechanisms to predict contention-induced performance degradation. In this work, we find that prior performance prediction frameworks suffer from poor accuracy on modern architectures and NFs because they treat memory as a monolithic whole. In addition, we show that, in practice, there exist multiple components of the memory subsystem that can separately induce contention. By precisely characterizing (1) the pressure each NF applies on the server's shared hardware resources (contentiousness) and (2) how susceptible each NF is to performance drop due to competing contentiousness (sensitivity), we develop SLOMO, a multivariable performance prediction framework for Network Functions. We show that relative to prior work SLOMO reduces prediction error by 2-5x and enables 6-14% more efficient cluster utilization. SLOMO's codebase can be found at https://github.com/cmu-snap/SLOMO.