# \[2021 EuroMLSys] Interference-Aware Scheduling for Inference Serving ## Summary 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 ![](/files/qZm0sHtOTwm2koAlUhak) ![](/files/gEG7V5ZkDDY5uX5vPnVz) 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). ## Evaluation ![](/files/sanQ09lzrhVJzvw5jFKQ) ## Links & References * [Paper PDF](https://dl.acm.org/doi/pdf/10.1145/3437984.3458837) --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://blog.ruipan.xyz/machine-learning-systems/machine-learning-systems-index/2021-euromlsys-interference-aware-scheduling-for-inference-serving.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.