Edge‐adaptable serverless acceleration for machine learning Internet of Things applications

Serverless computing is an emerging event‐driven programming model that accelerates the development and deployment of scalable web services on cloud computing systems. Though widely integrated with the public cloud, serverless computing use is nascent for edge‐based, Internet of Things (IoT) deploym...

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Bibliographic Details
Published in:Software, practice & experience practice & experience, 2021-09, Vol.51 (9), p.1852-1867
Main Authors: Zhang, Michael, Krintz, Chandra, Wolski, Rich
Format: Article
Language:English
Subjects:
Cloud computing
Cloud functions
Feedback control
GPU
Image processing
Internet of Things
IoT
Machine learning
Object recognition
Scheduling
Serverless
Web services
Workload
Workloads
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Summary:Serverless computing is an emerging event‐driven programming model that accelerates the development and deployment of scalable web services on cloud computing systems. Though widely integrated with the public cloud, serverless computing use is nascent for edge‐based, Internet of Things (IoT) deployments. In this work, we present STOIC (serverless teleoperable hybrid cloud), an IoT application deployment and offloading system that extends the serverless model in three ways. First, STOIC adopts a dynamic feedback control mechanism to precisely predict latency and dispatch workloads uniformly across edge and cloud systems using a distributed serverless framework. Second, STOIC leverages hardware acceleration (e.g., GPU resources) for serverless function execution when available from the underlying cloud system. Third, STOIC can be configured in multiple ways to overcome deployment variability associated with public cloud use. We overview the design and implementation of STOIC and empirically evaluate it using real‐world machine learning applications and multitier IoT deployments (edge and cloud). Specifically, we show that STOIC can be used for training image processing workloads (for object recognition)—once thought too resource‐intensive for edge deployments. We find that STOIC reduces overall execution time (response latency) and achieves placement accuracy that ranges from 92% to 97%.
ISSN:0038-0644
1097-024X
DOI:10.1002/spe.2944