# Seer: Proactive Revenue-Aware Scheduling for Live Streaming Services in Crowdsourced Cloud-Edge Platforms

arXiv: https://arxiv.org/abs/2402.14619 · HTML: https://mlsanity.com/paper/2402.14619/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2024.

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

- [HRS: Hybrid Representation Framework with Scheduling Awareness for Time Series Forecasting in Crowdsourced Cloud-Edge Platforms](https://mlsanity.com/paper/2508.12839/) (2025)
- [ScaleDL: Towards Scalable and Efficient Runtime Prediction for Distributed Deep Learning Workloads](https://mlsanity.com/paper/2511.04162/) (2025)
- [CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters](https://mlsanity.com/paper/2604.16400/) (2026)

## Abstract (arXiv)

As live streaming services skyrocket, Crowdsourced Cloud-edge service Platforms (CCPs) have surfaced as pivotal intermediaries catering to the mounting demand. Despite the role of stream scheduling to CCPs' Quality of Service (QoS) and throughput, conventional optimization strategies struggle to enhancing CCPs' revenue, primarily due to the intricate relationship between resource utilization and revenue. Additionally, the substantial scale of CCPs magnifies the difficulties of time-intensive scheduling. To tackle these challenges, we propose Seer, a proactive revenue-aware scheduling system for live streaming services in CCPs. The design of Seer is motivated by meticulous measurements of real-world CCPs environments, which allows us to achieve accurate revenue modeling and overcome three key obstacles that hinder the integration of prediction and optimal scheduling. Utilizing an innovative Pre-schedule-Execute-Re-schedule paradigm and flexible scheduling modes, Seer achieves efficient revenue-optimized scheduling in CCPs. Extensive evaluations demonstrate Seer's superiority over competitors in terms of revenue, utilization, and anomaly penalty mitigation, boosting CCPs revenue by 147% and expediting scheduling 3.4 × faster.