# Move Fast and Meet Deadlines: Fine-grained Real-time Stream Processing with Cameo

arXiv: https://arxiv.org/abs/2010.03035 · HTML: https://mlsanity.com/paper/2010.03035/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Oct 2020.

## Built on by (most cited first, 3 of 3)

- [Quiver: Supporting GPUs for Low-Latency, High-Throughput GNN Serving with Workload Awareness](https://mlsanity.com/paper/2305.10863/) (2023)
- [Distributed intelligence on the Edge-to-Cloud Continuum: A systematic literature review](https://mlsanity.com/paper/2205.01081/) (2022)
- [Fast and Flexible Human Pose Estimation with HyperPose](https://mlsanity.com/paper/2108.11826/) (2021)

## Abstract (arXiv)

Resource provisioning in multi-tenant stream processing systems faces the dual challenges of keeping resource utilization high (without over-provisioning), and ensuring performance isolation. In our common production use cases, where streaming workloads have to meet latency targets and avoid breaching service-level agreements, existing solutions are incapable of handling the wide variability of user needs. Our framework called Cameo uses fine-grained stream processing (inspired by actor computation models), and is able to provide high resource utilization while meeting latency targets. Cameo dynamically calculates and propagates priorities of events based on user latency targets and query semantics. Experiments on Microsoft Azure show that compared to state-of-the-art, the Cameo framework: i) reduces query latency by 2.7X in single tenant settings, ii) reduces query latency by 4.6X in multi-tenant scenarios, and iii) weathers transient spikes of workload.