Edge‐adaptable serverless acceleration for machine learning Internet of Things applications. (17th December 2020)

• Record Type: Journal Article
• Authors: Zhang, Michael; Krintz, Chandra; Wolski, Rich
• Other Names: Ilager Shashikant guestEditor.; Stankovski Vlado guestEditor.; Pallickarar Shrideep guestEditor.; Buyya Rajkumar guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 9(2021)
• Page Start: 1852
• Page End: 1867
• Publication Date: 2020-12-17
• Subjects: Cloud functions -- GPU -- IoT -- Scheduling -- Serverless; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2944
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: 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%.