System logs play a critical role in maintaining the reliability of software systems. Fruitful studies have explored automatic log-based anomaly detection and achieved notable accuracy on benchmark datasets. However, when applied to large-scale cloud systems, these solutions face limitations due to high resource consumption and lack of adaptability to evolving logs. In this paper, we present an accurate, lightweight, and adaptive log-based anomaly detection framework, referred to as SeaLog. Our method introduces a Trie-based Detection Agent (TDA) that employs a lightweight, dynamically-growing trie structure for real-time anomaly detection. To enhance TDA's accuracy in response to evolving log data, we enable it to receive feedback from experts. Interestingly, our findings suggest that contemporary large language models, such as ChatGPT, can provide feedback with a level of consistency comparable to human experts, which can potentially reduce manual verification efforts. We extensively evaluate SeaLog on two public datasets and an industrial dataset. The results show that SeaLog outperforms all baseline methods in terms of effectiveness, runs 2X to 10X faster and only consumes 5% to 41% of the memory resource.

As modern software systems continue to grow in terms of complexity and volume, anomaly detection on multivariate monitoring metrics, which profile systems' health status, becomes more and more critical and challenging. In particular, the dependency between different metrics and their historical patterns plays a critical role in pursuing prompt and accurate anomaly detection. Existing approaches fall short of industrial needs for being unable to capture such information efficiently. To fill this significant gap, in this paper, we propose CMAnomaly, an anomaly detection framework on multivariate monitoring metrics based on collaborative machine. The proposed collaborative machine is a mechanism to capture the pairwise interactions along with feature and temporal dimensions with linear time complexity. Cost-effective models can then be employed to leverage both the dependency between monitoring metrics and their historical patterns for anomaly detection. The proposed framework is extensively evaluated with both public data and industrial data collected from a large-scale online service system of Huawei Cloud. The experimental results demonstrate that compared with state-of-the-art baseline models, CMAnomaly achieves an average F1 score of 0.9494, outperforming baselines by 6.77% to 10.68%, and runs 10X to 20X faster. Furthermore, we also share our experience of deploying CMAnomaly in Huawei Cloud.

HUAWEI CLOUD's Enterprise Intelligence (EI) has achieved strong results in numerous industry competitions and evaluations. HUAWEI CLOUD has invested heavily in basic research AI in three domains: computer vision, speech and semantics, and decision optimization. To help AI empower all industries, the ModelArts enabling platform supports plug-and-play deployment of HUAWEI CLOUD's research results in areas such as automatic machine learning, small sample learning, federated learning, and pre-training models. In the area of perception, HUAWEI CLOUD continues to be an industry-leader in ImageNet large-scale image classification, WebVision large-scale network image classification, MS-COCO two-dimensional object detection, nuScenes three-dimensional object detection, and visual pre-training model verification, including downstream classification, detection, and segmentation. Perception models driven by ModelArts have been widely used in sectors such as medical image analysis, oil and gas exploration, and fault detection in manufacturing. In cognition, HUAWEI CLOUD integrates industry data based on its expertise in semantic analysis and knowledge graphs.

Huawei has launched a South African-based Cloud and Artificial Intelligence (AI) Innovation Centre to drive innovation, knowledge transfer and economic growth through app development in the AI industry. The announcement was made by Ray Rui, president of Huawei Cloud Africa region, during the Huawei Cloud Summit Africa 2020, an online event to unpack the opportunities of cloud computing for African business under the theme "Building an Intelligent Africa". "AI will be critical to social evolution and industrial growth in future," said Rui. "We also believe that when you grow economic opportunities, everyone benefits. For this reason, we are opening the Huawei Cloud & AI Innovation Centre to application developers across all economic sectors." The new centre will be based at Huawei's South African headquarters in Woodmead, Johannesburg, but developers across Africa will be able to access the centre remotely. It will teach AI application best practice, link developers to markets, support AI supply chains, develop talent and support application innovation.

Huawei Cloud joins in the fight against COVID-19 using technology that includes cloud and artificial intelligence (AI). The company crafted an international action plan which will allow collaborators to use AI and cloud services for free. "Huawei Cloud has been working with partners in China to use innovative technologies such as cloud and AI to fight the pandemic and has accumulated practical experience with AI-assisted CT scan analysis, drug discovery, online education, and telecommuting technologies, " said Deng Tao, president of Huawei Cloud Global Market. "Now, we are launching this international action plan to share our practical experience with the international market. We will make every effort to leverage technology to help our customers around the world cope with the challenges faced in the midst of this crisis."

We are at the threshold of a fully connected, intelligent world, where innovation happens in the blink of an eye, and the impact on every person, home, and organization is nothing short of profound. An intelligent world is right around the corner, with wide application of AI and 5G across all industries. More and more companies have come to realize the value of AI and 5G, and with eagerness to leverage them, are now focusing on how technologies can accelerate their migration to the cloud.