SWITCH

Description

Addressing runtime uncertainties in Machine Learning-Enabled Systems (MLS) is crucial for maintaining Quality of Service (QoS). The Machine Learning Model Balancer is a concept that addresses these uncertainties by facilitating dynamic ML model switching, showing promise in improving QoS in MLS. Leveraging this concept, this paper introduces SWITCH, an exemplar developed to enhance self-adaptive capabilities in such systems through dynamic model switching in runtime. SWITCH is designed as a comprehensive web service, catering to a broad range of ML scenarios, with its implementation demonstrated through an object detection use case. SWITCH provides researchers a flexible platform to apply and evaluate their ML model switching strategies, aiming to enhance QoS in MLS. SWITCH features advanced input handling, real-time data processing, and logging for adaptation metrics. With its interactive realtime dashboard, SWITCH offers researchers a user-friendly interface for experiment management and system observability for MLS. This paper details SWITCH’s architecture, self-adaptation strategies through ML model switching, and its empirical validation through case study, illustrating its potential to improve QoS in MLS. By enabling a hands-on approach to study adaptive behaviors in ML systems, SWITCH contributes a valuable tool to the SEAMS community for research into self-adaptive mechanisms and their practical applications

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