智能工业4.0应用在云计算系统上的性能分析
Analyzing the Performance of Smart Industry 4.0 Applications on Cloud Computing Systems
摘要：
基于云的深度神经网络（DNN）应用程序可以进行延迟敏感的推理，正在成为工业4.0不可或缺的一部分。由于云计算环境固有的多租户和资源异构性，基于DNN的应用程序的推理时间是随机的。这种随机性，如果不捕获，可能会导致低服务质量（QoS），甚至在关键部门，如石油和天然气行业的灾难。为了使工业4.0更加强大，解决方案架构师和研究人员需要了解基于DNN的应用程序的行为，并捕获其推理时间中存在的随机性。因此，在本研究中，我们从两个角度对推理时间进行了描述性分析。首先，我们执行以应用程序为中心的分析，并对Amazon和Chameleon云中四个截然不同的DNN应用程序的执行时间进行统计建模。其次，我们采取以资源为中心的方法，并分析基于速率的指标，以百万指令每秒（MIPS）的形式在云中的异构机器。通过Jackknife和Bootstrap重采样方法实现的这种非参数建模为异构云机提供了MIPS的置信区间。这项研究的结果可以帮助研究人员和云解决方案架构师开发解决方案，这些解决方案对云中DNN应用程序推理时间的随机性具有鲁棒性，并且可以为用户提供更高的QoS并避免意外结果。
英文摘要：
Cloud-based Deep Neural Network (DNN) applications that make latency-sensitive inference are becoming an indispensable part of Industry 4.0. Due to the multi-tenancy and resource heterogeneity, both inherent to the cloud computing environments, the inference time of DNN-based applications are stochastic. Such stochasticity, if not captured, can potentially lead to low Quality of Service (QoS) or even a disaster in critical sectors, such as Oil and Gas industry. To make Industry 4.0 robust, solution architects and researchers need to understand the behavior of DNN-based applications and capture the stochasticity exists in their inference times. Accordingly, in this study, we provide a descriptive analysis of the inference time from two perspectives. First, we perform an application-centric analysis and statistically model the execution time of four categorically different DNN applications on both Amazon and Chameleon clouds. Second, we take a resource-centric approach and analyze a rate-based metric in form of Million Instruction Per Second (MIPS) for heterogeneous machines in the cloud. This non-parametric modeling, achieved via Jackknife and Bootstrap re-sampling methods, provides the confidence interval of MIPS for heterogeneous cloud machines. The findings of this research can be helpful for researchers and cloud solution architects to develop solutions that are robust against the stochastic nature of the inference time of DNN applications in the cloud and can offer a higher QoS to their users and avoid unintended outcomes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
