Failure prediction is crucial for ensuring the stability of cloud computing systems and has garnered extensive attention from both academia and industry. Generally, data used for prediction includes two modalities: 1) Text data, such as logs; and, 2) Numerical data, such as error counts and monitoring metrics. However, most existing failure prediction algorithms for cloud computing only focus on a single modality. The lack of high-quality multimodal datasets from real-world production environments constrains academic research on multimodal failure prediction. To fill this gap, this paper releases a large multimodal dataset of operation data from the Alibaba cloud computing platform, namely, Logs and Metrics Integration Dataset (LMID). It consists of 100 million pieces of logs (textual data) and 37 dimensions of monitoring metrics (numerical data) from 220,000 physical machines. To our knowledge, it is the first multimodal dataset for cloud computing system failure prediction, and is expected to greatly benefit the community. This paper provides a detailed introduction to the construction of LMID, its contents, and the performance of state-of-the-art algorithms on it. It also conducts extensive experiments to reveal a new insight that cross-modality connections are effective for failure prediction. LMID is now available at https://huggingface.co/datasets/AliyunECSAlgos/LMID.

Log-based failure prediction helps identify and mitigate system failures ahead of time, increasing the reliability of cloud elastic computing systems.However, most existing log-based failure prediction approaches only focus on semantic information, and do not make full use of the information contained in the timestamps of log messages.This paper proposes time-aware attention-based transformer (TAAT), a failure prediction approach that extracts semantic and temporal information simultaneously from log messages and their timestamps.TAAT first tokenizes raw log messages into specific exceptions, and then performs: 1) exception sequence embedding that reorganizes the exceptions of each node as an ordered sequence and converts them to vectors; 2) time relation estimation that computes time relation matrices from the timestamps; and, 3) time-aware attention that computes semantic correlation matrices from the exception sequences and then combines them with time relation matrices.Experiments on Alibaba Cloud demonstrated that TAAT achieves an approximately 10% performance improvement compared with the state-of-the-art approaches.TAAT is now used in the daily operation of Alibaba Cloud.Moreover, this paper also releases the real-world cloud computing failure prediction dataset used in our study, which consists of about 2.7 billion syslogs from about 300,000 node controllers during a 4-month period.To our * Both authors contributed equally to this research.

The modern cloud computing system has evolved into a highly dynamic and complex ecosystem with thousands of modules. Modifications and updates to these modules occur every day to accommodate customer needs. Unfortunately, these frequent changes may introduce anomalies to the system, whose diffusion can undermine the system performance and even cause system outage. Though very important, it is challenging to detect anomalies at the early stages and locate their root causes, due to the complexity of the cloud ecosystem and the huge number of attribute combinations. This paper proposes DARC for high-dimensional diffusing anomaly detection and root cause location in cloud computing systems. DARC uses first two-stage percentile analysis and Mann-Kendall score thresholding to detect rare anomalies, and then a bottom-up search strategy with three computational complexity reduction techniques to efficiently locate the root causes. Extensive experiments showed that DARC is able to accurately and efficiently locate root causes of diffusing anomalies. It has been successfully used in the daily practice of Alibaba Cloud, one of the world’s largest cloud computing service providers.

Many machine learning (ML) applications rely on large amounts of personal data for training and inference. Among the most intimate exploited data sources is electroencephalogram (EEG) data. The emergence of consumer -grade, low-cost brain -computer interfaces (BCIs) and corresponding software development kits' is bringing the use of BCI within reach of application developers. The access that BCI applications have to neural signals rightly raises privacy concerns. Application developers can easily gain knowledge beyond the professed scope from unprotected EEG signals, including passwords, ATM PINs, and other personal data. The challenge is how to engage in meaningful ML with EEG data while protecting the privacy of users.