This work addresses correlation bias and causal effect confounding in advertising recommendation systems and presents a causal learning–based recommendation framework. We first examine the limitations of conventional recommendation algorithms in complex advertising environments, where confounding variables and exposure bias often prevent models from capturing users’ true preferences. To tackle these issues, we design a unified embedding architecture that jointly represents user, advertisement, and contextual features, and incorporates a structural causal graph to explicitly model dependencies among variables. During model training, causal consistency regularization and inverse propensity weighting are integrated to mitigate the impact of biased exposure mechanisms and non-uniform sampling. A joint optimization objective is further formulated to couple click-through rate prediction with causal consistency estimation, enabling robust causal effect learning without sacrificing predictive accuracy. Extensive experiments on large-scale advertising datasets demonstrate that the proposed approach consistently outperforms several representative baselines in terms of Precision@10, Recall@10, NDCG@10, and MAP, while exhibiting strong robustness under multi-dimensional sensitivity analysis. Overall, this study highlights the practical value of causal modeling and consistency-aware learning in advertising recommendation and offers a computationally grounded approach for improving both interpretability and fairness in recommendation systems.

By decoupling services and enabling elastic deployment, microservice architecture improves system scalability and evolutionary capability. At the same time, it substantially increases operational complexity. Failures often exhibit cross service propagation and a mismatch between observed symptoms and underlying root causes. To address the heterogeneity and fragmentation of multi source observability data such as logs, metrics, and distributed traces, this study proposes a unified modeling and intelligent root cause localization method for microservice systems. The approach treats each service as a basic modeling unit and maps heterogeneous observations into a shared representation space. Service dependency structure is explicitly incorporated to characterize system state at a global level. Through structure aware modeling on the dependency graph, anomaly information is propagated and constrained along real invocation relations. This design enables more accurate separation of local disturbances from structural anomalies. In addition, a consistency based measure derived from state deviation is constructed to score service anomalies. Dependency relations are then used for attribution and ranking, which unifies root cause localization and impact analysis within a single framework. Comparative results show that the proposed method achieves more stable and consistent advantages across multiple evaluation metrics. It captures anomaly propagation patterns in microservice systems more effectively and provides a unified and structure aware solution for intelligent diagnosis of complex distributed systems.

We address a key challenge in cloud native microservice anomaly detection. Existing methods struggle to characterize cross-service dependency propagation and complex temporal dynamics. We propose a temporal representation framework based on cross-service contrastive learning. The method first constructs multi-scale views from service call relations and historical runtime data. It embeds system-level collaborative behaviors into temporal window representations. This yields structure-enhanced input features. It then applies a dual view contrastive encoding strategy with weak augmentation across different time windows. This strategy pulls normal collaborative patterns closer and separates abnormal deviation patterns in the feature space. It improves sensitivity and robustness. The framework further performs unsupervised anomaly scoring through feature aggregation and service-level prototype construction. This avoids extra labeling costs and reduces dependence on manual thresholds. To evaluate the framework, we conduct systematic validation using metrics, logs, and distributed traces from a real open source microservice benchmark system. We also perform multi-dimensional hyperparameter sensitivity analyses across learning rate, batch size, time window length, and the temperature coefficient of the contrastive loss. These analyses reveal stability and adaptability under different dynamic conditions. In comparison with existing baseline models, the method achieves higher accuracy, stronger recall, and better overall performance. This confirms the advantage of cross-service contrastive strategies for capturing anomaly propagation patterns and structural characteristics. Overall, the proposed unified structure of temporal representations and contrastive learning mechanism enables effective unsupervised anomaly detection in collaborative microservice environments. The framework shows strong discriminative capacity and robustness under complex system operations. It provides a reproducible technical solution for improving intelligent operations in cloud native systems.

This study addresses the challenge of forecasting backend service metrics in highly dynamic and disturbed environments and proposes a robust time series prediction framework based on uncertainty estimation. The method first applies a multi-scale encoding structure to decompose and represent service metric sequences, allowing the model to capture mixed characteristics of trends, periodic patterns, and noise disturbances. It then constructs a cross-sequence dependency modeling module to describe structural coupling among multiple metrics so that the model can identify implicit interaction patterns across different service nodes in backend systems. On this basis, the framework outputs both the mean and variance of future metrics through distributed prediction heads, enabling joint characterization of predictions and their associated risk ranges. A noise response mechanism is incorporated to enhance model stability under anomalous disturbances. To improve the controllability of the overall prediction process, a risk adjustment module is designed to transform uncertainty signals into actionable adjustment factors, allowing the model to maintain consistent outputs in high volatility environments. The experiments evaluate hyperparameter sensitivity, environmental sensitivity, and data sensitivity. The results show that the proposed framework maintains high stability and robustness under multiple source disturbances and effectively suppresses error propagation caused by anomalous samples, structural changes, and system scaling. By jointly modeling point predictions and risk estimation, the study verifies the effectiveness of an uncertainty-driven robust forecasting framework for backend metric management and system reliability, providing a scalable technical path for data-driven analysis in complex systems.

Identifying abnormal behaviors plays a vital role in ensuring cloud infrastructure remains secure and operationally stable. Conventional methods that aggregate data at a single location create substantial privacy concerns, especially within shared cloud platforms hosting multiple organizations with confidential operational information. This paper proposes HierFedDP, a hierarchical federated learning framework integrated with a two-stage differential privacy mechanism for privacy-preserving anomaly detection in cloud services. Our approach employs a three-tier architecture consisting of local clients, edge servers, and a central cloud server, where clients apply local differential privacy to their updates before transmission. We introduce an edge aggregation frequency parameter that enables edge servers to perform multiple local aggregation rounds before communicating with the central cloud. Experiments on the CICIDS2017 dataset demonstrate that HierFedDP achieves detection performance comparable to standard local differential privacy approaches while reducing wide-area network (WAN) communication overhead by 49%. This significant communication reduction, achieved without sacrificing privacy guarantees or detection accuracy, makes HierFedDP particularly suitable for bandwidth-constrained, geo-distributed cloud deployments.