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.

翻译：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且跨服务影响显著的特点，提出一种面向微服务拓扑的结构化时序联合负载预测框架。该方法将系统表征为时变服务调用图与多元负载序列的耦合实体，基于服务层级观测构建邻域聚合与全局汇总视图，形成实例、服务及集群层级的负载分层表征。通过统一的序列编码器对多尺度历史上下文进行建模。为强化调用依赖关系的表达，框架在注意力计算中引入轻量级结构先验，从而更有效地捕捉沿调用链的负载传播与累积效应，同时保持对局部突发与整体趋势的一致性建模。训练目标采用多目标回归策略，联合优化服务层级与集群层级的预测结果以提升跨粒度稳定性。我们进一步对关键结构与训练超参数进行单因素敏感性分析，系统考察时间窗口长度、编码深度及正则化强度的影响。实验结果验证了多粒度融合与结构注入的必要性，并明确了其有效配置范围。总体而言，该框架为云环境下的容量评估、资源编排及运行时态势理解提供了可复用的建模范式与实施路径。