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 $3.4 \times$ faster.

翻译：随着直播服务的迅猛发展，众包云边服务平台（CCP）作为满足日益增长需求的关键中介应运而生。尽管流调度对CCP的服务质量（QoS）和吞吐量至关重要，但由于资源利用率与收入之间的复杂关系，传统优化策略难以提升CCP的收益。此外，CCP的庞大规模加剧了耗时调度的难度。为应对这些挑战，我们提出Seer——一种应用于CCP直播服务的主动式收入感知调度系统。Seer的设计源于对真实CCP环境的精细测量，这使得我们能够实现精确的收入建模，并克服阻碍预测与最优调度集成的三大关键障碍。通过创新的预调度-执行-重调度范式及灵活调度模式，Seer在CCP中实现了高效的收入优化调度。大量评估表明，Seer在收入、资源利用率及异常惩罚缓解方面优于竞品，可将CCP收入提升147%，并将调度速度提升3.4倍。