STARIXNet通过多指标时序建模，实现云平台实时资源分配优化。
STARIXNet: Multivariate and Multi-attribute Deep Learning Approach to Real-Time Resource Allocation in Cloud Platforms
- 融合多维度系统指标的时序模式，构建轻量级神经网络决策模型。
- 在沃尔玛生产环境实测，资源成本降低10%至50%，服务稳定性显著提升。
- 适合需要高稳定性和成本控制的大型云服务系统部署。
云平台中微服务的智能弹性伸缩对降低计算成本、避免服务中断至关重要。现有方案多局限于单一指标（如仅依赖CPU使用率）进行扩容决策，且将问题视为纯预测任务，过度关注预测精度，忽视低估风险与响应延迟。此外，部分方案计算复杂度高，难以在大规模实时场景中应用。为此，我们提出STARIXNet，一种轻量级神经网络，通过捕捉多个系统指标间的时空关联，在多变量空间中指导资源分配。该模型综合建模季节性、时间自回归、积分及外生变量等特征（STARI-X），并采用聚合策略生成最终扩缩决策，优先保障服务稳定性与成本效率，而非单纯追求预测准确率。我们在真实环境中对比验证了STARIXNet性能：已在沃尔玛关键生产微服务中部署，实现10%至50%的成本节约，并带来服务稳定性与用户体验的无形提升。
原文摘要 · Abstract (English)
Intelligent scaling of microservices in cloud platforms is crucial for mitigating escalating compute costs while avoiding service disruptions. Current solutions are limited to the univariate space, typically focusing on CPU usage alone to drive scaling decisions. Moreover, they address the problem as a purely forecasting task, focusing on prediction precision while neglecting the greater risks of underestimation and delays in system responsiveness. Alternative solutions are computationally complex, making them impractical for large-scale, real-time deployments. To address these challenges, we present STARIXNet, a lightweight neural network that guides resource allocation decisions in the multivariate space by capturing spatio-temporal relationships among multiple system metrics. STARIXNet models multiple quasi-dependent attributes, in particular the (S)easonal, (T)emporal, (A)uto-(R)egressive (I)ntegrated, and e(X)ogenous patterns, then implements an aggregation policy to finalize scaling decisions, prioritizing service stability, followed by cost-efficiency, over raw forecast accuracy. We empirically demonstrate the performance of STARIXNet by benchmarking against existing solutions in real-world settings. STARIXNet is deployed for critical production microservices at Walmart achieving tangible savings ranging from 10\% to 50\%, in addition to intangible benefits through improved service stability and customer experience.
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