Orchestrating networked machine learning applications using Autosteer

Wen, Zhenyu; Hu, Haozhen; Yang, Renyu; Qian, Bian; Sham, Ringo W.H.; Rui, Sun; Xu, Jie; Patel, Pankesh; Rana, Omer; Dustdar, Schahram; Ranjan, Rajiv

doi:http://dx.doi.org/10.1109/MIC.2022.3180907

Orchestrating networked machine learning applications using Autosteer

Citation

Wen Z, Hu H, Yang R, Qian B, Sham RWH, Sun R, Xu J, Patel P, Rana O, Dustdar S, Ranjan R. Orchestrating networked machine learning applications using Autosteer. IEEE Internet Comput. 2022;26(6):51-8. DOI: 10.1109/MIC.2022.3180907

Abstract

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.