提出兼顾调度的混合表示框架，提升云边平台负载预测精度。
HRS: Hybrid Representation Framework with Scheduling Awareness for Time Series Forecasting in Crowdsourced Cloud-Edge Platforms
- 融合数值与图像表示，捕捉极端负载动态变化
- 引入调度感知损失函数，降低峰值期服务违规率63.1%
- 适合需要高可靠资源调度的实时流服务系统
随着流媒体服务的快速发展，网络负载表现出高度时变和突发特性，给众包云边平台（CCPs）的服务质量（QoS）保障带来严峻挑战。尽管CCPs采用预测-调度架构以提升QoS与收益，但在流量高峰期间仍难以实现精准负载预测。现有方法或仅最小化平均绝对误差，导致峰值期资源不足引发服务等级协议（SLA）违约；或采取保守的过度配置策略，虽降低风险却增加资源开销。为此，本文提出HRS——一种具有调度感知的混合表示框架，通过整合数值与图像表示，更有效地捕捉极端负载动态。同时设计调度感知损失函数（SAL），反映预测误差的非对称影响，引导更利于调度决策的预测结果。在四个真实数据集上的大量实验表明，HRS持续优于十种基线方法，达到当前最佳性能，将SLA违规率降低63.1%，总利润损失减少32.3%。
原文摘要 · Abstract (English)
With the rapid proliferation of streaming services, network load exhibits highly time-varying and bursty behavior, posing serious challenges for maintaining Quality of Service (QoS) in Crowdsourced Cloud-Edge Platforms (CCPs). While CCPs leverage Predict-then-Schedule architecture to improve QoS and profitability, accurate load forecasting remains challenging under traffic surges. Existing methods either minimize mean absolute error, resulting in underprovisioning and potential Service Level Agreement (SLA) violations during peak periods, or adopt conservative overprovisioning strategies, which mitigate SLA risks at the expense of increased resource expenditure. To address this dilemma, we propose HRS, a hybrid representation framework with scheduling awareness that integrates numerical and image-based representations to better capture extreme load dynamics. We further introduce a Scheduling-Aware Loss (SAL) that captures the asymmetric impact of prediction errors, guiding predictions that better support scheduling decisions. Extensive experiments on four real-world datasets demonstrate that HRS consistently outperforms ten baselines and achieves state-of-the-art performance, reducing SLA violation rates by 63.1% and total profit loss by 32.3%.
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