Sentinel通过主动检测异常，提升云边协同直播调度的效率与收益。
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms
- 分预调度和后调度两阶段，提前识别异常并生成应对策略。
- 实测异常频率降70%，收入提升74%，调度速度翻倍。
- 适合大规模、不稳定的云边直播平台使用。
随着直播服务的快速增长，众包云边服务平台（CCPs）在满足日益增长的需求方面发挥着越来越重要的作用。尽管直播调度对优化CCPs收益至关重要，但大多数优化策略因不稳定平台中的各种异常而难以取得实际效果。此外，CCPs的巨大规模进一步加剧了在时敏调度中异常检测的难度。为此，本文提出Sentinel，一种基于主动异常检测的调度框架。Sentinel将调度过程建模为两阶段预-后调度范式：在预调度阶段，进行异常检测并构建策略池；在后调度阶段，当请求到达时，基于预先生成的策略触发合适的调度以完成调度过程。在真实数据集上的大量实验表明，Sentinel显著将异常频率降低70%，收入提升74%，调度速度提高一倍。
原文摘要 · Abstract (English)
With the rapid growth of live streaming services, Crowdsourced Cloud-edge service Platforms (CCPs) are playing an increasingly important role in meeting the increasing demand. Although stream scheduling plays a critical role in optimizing CCPs' revenue, most optimization strategies struggle to achieve practical results due to various anomalies in unstable CCPs. Additionally, the substantial scale of CCPs magnifies the difficulties of anomaly detection in time-sensitive scheduling. To tackle these challenges, this paper proposes Sentinel, a proactive anomaly detection-based scheduling framework. Sentinel models the scheduling process as a two-stage Pre-Post-Scheduling paradigm: in the pre-scheduling stage, Sentinel conducts anomaly detection and constructs a strategy pool; in the post-scheduling stage, upon request arrival, it triggers an appropriate scheduling based on a pre-generated strategy to implement the scheduling process. Extensive experiments on realistic datasets show that Sentinel significantly reduces anomaly frequency by 70%, improves revenue by 74%, and doubles the scheduling speed.
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