论文标题

部分可观测时空混沌系统的无模型预测

BenchFaaS: Benchmarking Serverless Functions in an Edge Computing Network Testbed

论文作者

Carpio, Francisco, Michalke, Marc, Jukan, Admela

论文摘要

储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。

The serverless computing model has evolved as one of the key solutions in the cloud for fast autoscaling and capacity planning. In edge computing environments, however, the serverless model is challenged by the system heterogeneity and performance variability. In this paper, we introduce BenchFaaS, an open-source edge computing network testbed which automates the deployment and benchmarking of serverless functions. Our edge computing network considers a cluster of virtual machines and Raspberry Pis, and is designed to benchmark serverless functions under different hardware and network conditions. We measure and evaluate: (i) overhead incurred by testbed, (ii) performance of compute intensive tasks, (iii) impact of application payload size, (iv) scalability, and (v) performance of chained serverless functions. We share the lessons learnt in engineering and implementing the testbed. We present the measurement results and analyze the impact of networked infrastructure on serverless performance. The measurements indicate that a properly dimensioned edge computing network can effectively serve as a serverless infrastructure.

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