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This paper introduces a structural-temporal joint load forecasting framework for cloud-native platforms, addressing the challenges of complex microservice invocation relations and multi-scale load fluctuations. The framework models the system as a time-evolving service invocation graph coupled with multivariate load sequences, constructing layered load representations across instance, service, and cluster levels. By injecting a structural prior into attention computation and employing a multi-objective regression strategy, the framework effectively captures load propagation and accumulation, achieving improved cross-granularity stability.
Injecting structural priors into attention mechanisms significantly improves load forecasting accuracy in cloud-native platforms by effectively capturing load propagation along service invocation chains.
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.
