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arXiv · 2404.00704

Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling

Kamran Razavi·Saeid Ghafouri·Max Mühlhäuser·Pooyan Jamshidi·Lin Wang
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Abstract

Mobile and IoT applications increasingly adopt deep learning inference to provide intelligence. Inference requests are typically sent to a cloud infrastructure over a wireless network that is highly variable, leading to the challenge of dynamic Service Level Objectives (SLOs) at the request level. This paper presents Sponge, a novel deep learning inference serving system that maximizes resource efficiency while guaranteeing dynamic SLOs. Sponge achieves its goal by applying in-place vertical scaling, dynamic batching, and request reordering. Specifically, we introduce an Integer Programming formulation to capture the resource allocation problem, providing a mathematical model of the relationship between latency, batch size, and resources. We demonstrate the potential of Sponge through a prototype implementation and preliminary experiments and discuss future works.

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Kamran Razavi, Saeid Ghafouri, Max Mühlhäuser, Pooyan Jamshidi, Lin Wang. 2024-03-31. Sponge: Inference Serving with Dynamic SLOs Using In-Place Vertical Scaling. https://doi.org/10.1145/3642970.3655833

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