Orchestrating Networked Machine Learning Applications Using Autosteer
Orchestrating Networked Machine Learning Applications Using Autosteer
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DOI：
10.1109/mic.2022.3180907
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发表时间：
2022-11
影响因子：
3.2
通讯作者：
Z. Wen;Haozhen Hu;Renyu Yang;Bin Qian;Ringo W. H. Sham;Rui Sun;Jie Xu;Pankesh Patel;O. Rana;S. Dustdar;R. Ranjan;E. Deelman
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文献类型：
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作者：
Z. Wen;Haozhen Hu;Renyu Yang;Bin Qian;Ringo W. H. Sham;Rui Sun;Jie Xu;Pankesh Patel;O. Rana;S. Dustdar;R. Ranjan;E. Deelman
描述了一种用于在分布式环境上编排联网的机器学习（ML）应用的平台。ML应用程序被转换为管理整个应用程序生命周期的自动化管道，并自动构建生产级实现。我们介绍了AUTOSTEER，这是一个软件平台，可以在各种硬件资源上部署ML应用程序-使用异构网络资源互连-跨云和边缘设备。设备布局优化和模型自适应用作控制动作，以支持应用程序需求并最大限度地提高异构计算资源上ML模型执行的性能。在运行时持续监控已部署应用程序的性能，以克服由于不正确的应用程序参数设置或模型衰减而导致的性能下降。三个真实世界的应用程序用于演示AUTOSTEER如何支持应用程序部署和运行时性能保证。
A platform for orchestrating networked machine learning (ML) applications over distributed environments is described. ML applications are transformed into automated pipelines that manage the whole application lifecycle and production-grade implementations are automatically constructed. We present AUTOSTEER, a software platform that can deploy ML applications on various hardware resources—interconnected using heterogeneous network resources—across cloud and edge devices. Device placement optimization and model adaptation are used as control actions to support application requirements and maximize the performance of ML model execution over heterogeneous computing resources. The performance of deployed applications is continually monitored at runtime to overcome performance degradation due to incorrect application parameter settings or model decay. Three real-world applications are used to demonstrate how AUTOSTEER can support application deployment and runtime performance guarantees.