Serving DNNs like Clockwork: Performance Predictability from the Bottom Up

• URL: http://arxiv.org/abs/2006.02464v2
• Date: Mon, 26 Oct 2020 15:52:20 GMT
• Title: Serving DNNs like Clockwork: Performance Predictability from the Bottom Up
• Authors: Arpan Gujarati, Reza Karimi, Safya Alzayat, Wei Hao, Antoine Kaufmann, Ymir Vigfusson, Jonathan Mace
• Abstract summary: Machine learning inference is becoming a core building block for interactive web applications. Existing model serving architectures use well-known reactive techniques to alleviate common-case sources of latency. We observe that inference using Deep Neural Network (DNN) models has deterministic performance.
• Score: 4.293235171619925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Machine learning inference is becoming a core building block for interactive web applications. As a result, the underlying model serving systems on which these applications depend must consistently meet low latency targets. Existing model serving architectures use well-known reactive techniques to alleviate common-case sources of latency, but cannot effectively curtail tail latency caused by unpredictable execution times. Yet the underlying execution times are not fundamentally unpredictable - on the contrary we observe that inference using Deep Neural Network (DNN) models has deterministic performance. Here, starting with the predictable execution times of individual DNN inferences, we adopt a principled design methodology to successively build a fully distributed model serving system that achieves predictable end-to-end performance. We evaluate our implementation, Clockwork, using production trace workloads, and show that Clockwork can support thousands of models while simultaneously meeting 100ms latency targets for 99.9999% of requests. We further demonstrate that Clockwork exploits predictable execution times to achieve tight request-level service-level objectives (SLOs) as well as a high degree of request-level performance isolation.

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