Intelligent Resource Scheduling for Co-located Latency-critical Services: A Multi-Model Collaborative Learning Approach


Lei Liu, Beihang University; Xinglei Dou, ICT, CAS; Yuetao Chen, ICT, CAS; Sys-Inventor Lab


Latency-critical services have been widely deployed in cloud environments. For cost-efficiency, multiple services are usually co-located on a server. Thus, run-time resource scheduling becomes the pivot for QoS control in these complicated co-location cases. However, the scheduling exploration space enlarges rapidly with the increasing server resources, making the schedulers hardly provide ideal solutions quickly. More importantly, we observe that there are “resource cliffs” in the scheduling exploration space. They affect the exploration efficiency and always lead to severe QoS fluctuations in previous schedulers. To address these problems, we propose a novel ML-based intelligent scheduler – OSML. It learns the correlation between architectural hints (e.g., IPC, cache misses, memory footprint, etc.), scheduling solutions and the QoS demands based on a data set we collected from 11 widely deployed services running on off-the-shelf servers. OSML employs multiple ML models to work collaboratively to predict QoS variations, shepherd the scheduling, and recover from QoS violations in complicated co-location cases. OSML can intelligently avoid resource cliffs during scheduling and reach an optimal solution much faster than previous approaches for co-located LC services. Experimental results show that OSML supports higher loads and meets QoS targets with lower scheduling overheads and shorter convergence time than previous studies.

FAST '23 Open Access Sponsored by

Open Access Media

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are posted after the event are also free and open to everyone. Support USENIX and our commitment to Open Access.

This content is available to:

@inproceedings {285744,
author = {Lei Liu and Xinglei Dou and Yuetao Chen},
title = {Intelligent Resource Scheduling for Co-located Latency-critical Services: A {Multi-Model} Collaborative Learning Approach},
booktitle = {21st USENIX Conference on File and Storage Technologies (FAST 23)},
year = {2023},
isbn = {978-1-939133-32-8},
address = {Santa Clara, CA},
pages = {153--166},
url = {},
publisher = {USENIX Association},
month = feb,

Presentation Video