[2021 EuroMLSys] Interference-Aware Scheduling for Inference Serving


This work proposes a scheduler for inference workloads on heterogeneous hardware. The scheduler is aware of and proactive to interferences between co-located jobs, therefore outperforming baseline policies like lease-loaded.

Background & Motivation

Inference serving schedulers co-locate models to improve resource utilization. However, the least-loaded scheduling policy, popular in the context of VM task scheduling, is agnostic to the interference/latency degradation created by co-location, thus yielding sub-optimal scheduling result.

Design & Implementation

By using a unified predictor instead of maintaining separate predictors for different co-location degrees and machine types, we are able to (1) reduce the efforts needed to train multiple predictors and (2) exploit the similarity across co-location configurations (e.g., the same models on an 8vCPU VM vs. a 32vCPU VM).


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