Network failure diagnosis is challenging yet critical for high-performance computing (HPC) systems. Existing methods cannot be directly applied to HPC scenarios due to data heterogeneity and lack of accuracy. This paper proposes a novel framework, called ClusterRCA, to localize culprit nodes and determine failure types by leveraging multimodal data. ClusterRCA extracts features from topologically connected network interface controller (NIC) pairs to analyze the diverse, multimodal data in HPC systems. To accurately localize culprit nodes and determine failure types, ClusterRCA combines classifier-based and graph-based approaches. A failure graph is constructed based on the output of the state classifier, and then it performs a customized random walk on the graph to localize the root cause. Experiments on datasets collected by a top-tier global HPC device vendor show ClusterRCA achieves high accuracy in diagnosing network failure for HPC systems. ClusterRCA also maintains robust performance across different application scenarios.

AIOps algorithms playa crucial role in the mainte-nance of microservice systems. Many previous benchmarks' per-formance leaderboard provides valuable guidance for selecting appropriate algorithms. However, existing AIOps benchmarks mainly utilize offline static datasets to evaluate algorithms. They cannot consistently evaluate the performance of algorithms using real-time datasets, and the operation scenarios for evaluation are static, which is insufficient for effective algorithm selection. To address these issues, we propose an evaluation-consistent and scenario-oriented evaluation framework named AIOpsArena. The core idea is to build a live microservice benchmark to generate real-time datasets and consistently simulate the specific operation scenarios on it. AIOpsArena supports different leaderboards by selecting specific algorithms and datasets according to the operation scenarios. It also supports the deployment of various types of algorithms, enabling algorithms hot-plugging. At last, we test AIOpsArena with typical microservice operation scenarios to demonstrate its efficiency and usability. Platform and a video demonstrating the functioning of AIOpsArena is available from https://github.com/AIOpsArena/aiopsarena.

Microservice invocation anomalies can have a detrimental impact on user experience and service revenue. While existing trace anomaly detection approaches typically focus on anomalies in response time and invocation structure, they often overlook the importance of using fine-grained features to detect anomalies. Additionally, trace data obtained from real-world scenarios is typically accompanied by noise, which can hinder the effectiveness of anomaly detection approaches. Furthermore, large-scale trace data can significantly impact model training efficiency. To address these challenges, we propose TraceSieve, an unsupervised trace anomaly detection method that accurately detects trace anomalies. Our approach leverages an auto-encoder architecture within an adversarial training framework to filter out noise data. Additionally, we integrate VGAE-EWC, which combines Variational Graph Auto-Encoder (VGAE) with Elastic Weight Consolidation (EWC), to overcome the challenges of enormous time consumption during the training phase. Finally, we localize the root cause of trace anomalies. Our proposed method is evaluated using two different datasets, and our results demonstrate that TraceSieve achieves an F1-score of 0.970 and 0.925, respectively, outperforming state-of-the-art trace anomaly detection approaches.

Abstract With the development of artificial intelligence technology, human–computer interaction technology through gestures, images and voices has gradually become a hot topic for discussion. A modified Yolov5s gesture recognition method is proposed in the field of human–computer cooperation by optimizing the network structure of Yolov5s backbone, CNN is replaced by Ghostbottleneck module to increase the target occlusion recognition rate. Secondly, tensor stitching is added to the output of Ghostbottleneck module for up sampling to strengthen the reuse of image features. Finally, the detection ability of the improved model in the face of complex environment is verified on the self‐made data set. Experimental results show that, the [email protected] (mean average precision) of the modified Yolov5s is 94.49%, the AP (average precision) is 94.2%. By comparing the Yolov5s algorithm, Yolov4 algorithm,Yolov3 algorithm and SSD algorithm, the detection accuracy of the modified method has been significantly improved, which can fully meet the application requirements of real‐time detection of gesture‐controlled robots.