  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.
