面向云原生平台的结构-时间联合负载预测人工智能框架
An Artificial Intelligence Framework for Joint Structural-Temporal Load Forecasting in Cloud Native Platforms
AI总结：
本文提出面向云原生平台的结构-时间联合负载预测框架，通过多粒度融合和结构注入提升负载预测稳定性，为容量评估和资源编排提供新方法。
摘要：
本研究针对云原生环境中微服务调用关系复杂、负载波动多尺度叠加以及跨服务影响显著的特性，提出了一种面向微服务拓扑的结构-时间联合负载预测框架。该方法将系统表示为一个随时间演化的服务调用图与多变量负载序列的耦合实体。它基于服务层面的观测构建了邻域聚合和全局汇总视图，形成实例、服务和集群层级的负载表示。统一的序列编码器建模多尺度的历史上下文。为加强调用依赖的表达，该框架在注意力计算中引入了轻量级的结构先验。这使得能够更有效地捕捉负载传播和累积沿调用链，同时保持局部爆发和整体趋势的一致建模。训练目标采用多目标回归策略，联合优化服务层面和集群层面的预测以提高跨粒度稳定性。我们进一步对关键结构和训练超参数进行了单因素敏感性分析。我们系统地检验了时间窗口长度、编码深度和正则化强度的影响。结果支持了多粒度融合和结构注入的必要性，并明确了其有效配置范围。总体而言，该框架为云环境中的容量评估、资源编排和运行时情境理解提供了可重用的建模范式和实现路径。
英文摘要：
This study targets cloud native environments where microservice invocation relations are complex, load fluctuations are multi-scale and superimposed, and cross-service impacts are significant. We propose a structured temporal joint load prediction framework oriented to microservice topology. The method represents the system as a coupled entity of a time-evolving service invocation graph and multivariate load sequences. It constructs neighborhood-aggregated and global summarized views based on service level observations. This forms layered load representations across instance, service, and cluster levels. A unified sequence encoder models multi-scale historical context. To strengthen the expression of invocation dependencies, the framework introduces a lightweight structural prior into attention computation. This enables more effective capture of load propagation and accumulation along invocation chains, while maintaining consistent modeling of local bursts and overall trends. The training objective adopts a multi-objective regression strategy that jointly optimizes service level and cluster level predictions to improve cross-granularity stability. We further conduct single-factor sensitivity analyses on key structural and training hyperparameters. We systematically examine the effects of time window length, encoding depth, and regularization strength. The results support the necessity of multi-granularity fusion and structural injection and clarify their effective configuration ranges. Overall, the framework provides a reusable modeling paradigm and implementation path for capacity assessment, resource orchestration, and runtime situational understanding in cloud environments.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
