# Characterizing Co-located Datacenter Workloads: An Alibaba Case Study

arXiv: https://arxiv.org/abs/1808.02919 · HTML: https://mlsanity.com/paper/1808.02919/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2018.

## Built on by (most cited first, 5 of 5)

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- [On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds](https://mlsanity.com/paper/2111.08759/) (2021)
- [Sustainability of Data Center Digital Twins with Reinforcement Learning](https://mlsanity.com/paper/2404.10786/) (2024)
- [Learning to Score: Tuning Cluster Schedulers through Reinforcement Learning](https://mlsanity.com/paper/2603.10545/) (2026)

## Abstract (arXiv)

Warehouse-scale cloud datacenters co-locate workloads with different and often complementary characteristics for improved resource utilization. To better understand the challenges in managing such intricate, heterogeneous workloads while providing quality-assured resource orchestration and user experience, we analyze Alibaba's co-located workload trace, the first publicly available dataset with precise information about the category of each job. Two types of workload---long-running, user-facing, containerized production jobs, and transient, highly dynamic, non-containerized, and non-production batch jobs---are running on a shared cluster of 1313 machines. Our multifaceted analysis reveals insights that we believe are useful for system designers and IT practitioners working on cluster management systems.