Online transaction processing (OLTP) database systems provide an effective solution to data support for online applications with high concurrency and low latency. An interruption or performance degradation of OLTP database systems may impact the availability of services and bring substantial economic loss. Thus, diagnosing the issue timely and mitigating it rapidly are essential for database administrators (DBAs). However, performance diagnosis for database systems is challenging due to numerous abnormal metrics, complex failure propagation, and high-performance requirements. Existing works relying on anomaly detection or causal graph construction cannot handle all these challenges simultaneously. In this paper, we propose an unsupervised learning-based method, CauseRank, to perform root cause localization with superior efficiency, high accuracy, and good interpretability. Two key techniques in CauseRank are a novel causal discovery algorithm named Group-based Greedy Equivalent Search (G-GES) incorporated with domain knowledge which treats metric groups as nodes to capture failure propagation and a simple yet effective ranking method named Causal Oriented Personalized PageRank (COPP). Extensive experiments on 97 real-world failure cases collected from a large-scale Oracle database demonstrate the effectiveness of CauseRank, achieving 82.5% top-3 accuracy and 93.8% top-5 accuracy and outperforming baseline approaches. The core idea and framework of CauseRank are generic and can be applied to other large-scale system components.

Using large-scale multi-dimensional data for root cause analysis (MDRCA) is vitally important for online software services. It helps operators narrow down the scope of anomalies and failures quickly and localize the root cause to a finer granularity. However, most existing MDRCA algorithms can only solve low-dimensional problems. When dealing with high-dimensional data, the complexity of these algorithms would significantly increase, and even some algorithms would no longer work. Intuitively, passing only a subset of attributes rather than full attributes can improve the performance of these MDRCA algorithms. However, it is challenging due to data imbalance and novel root cause attributes. To better understand the problem of root-cause-oriented attribute selection (RCOAS), we conduct a preliminary study based on real-world data. We find that there exist several straightforward rules to filter out some attributes. In addition, we reveal that existing approaches do not fit the requirements of RCOAS. Motivated by the study, we propose an RCOAS approach, RC-LIR, to select a subset of attributes for downstream algorithms. RC-LIR first performs rule-based selection. Then it improves a feature selection algorithm by two strategies, i.e., scaling up imbalanced data and considering the redundant cost. Experiments on 1000 real-world fault cases demonstrate that RC-LIR can achieve an F1-score of 0.88, outper-forming the baseline approaches by at least 0.15. Furthermore, our experiments with four widely adopted MDRCA algorithms show that integrating RC-LIR can lead to more effective and efficient MDRCA.

Fault localization is challenging in an online service system due to its monitoring data's large volume and variety and complex dependencies across/within its components (e.g., services or databases). Furthermore, engineers require fault localization solutions to be actionable and interpretable, which existing research approaches cannot satisfy. Therefore, the common industry practice is that, for a specific online service system, its experienced engineers focus on localization for recurring failures based on the knowledge accumulated about the system and historical failures. More specifically, 1) they can identify the underlying root causes and take mitigation actions when pinpointing a group of indicative metrics on the faulty component; 2) their diagnosis knowledge is roughly based on how one failure might affect the components in the whole system.

Incidents in online service systems could incur poor user experience and tremendous economic loss. To reduce the influence of incidents and guarantee service reliability, it is critical to identify root-cause metrics for engineers with clues to assist incident diagnosis. However, it is a challenging task due to the complicated dependencies and huge volume of various metrics in large-scale systems. Existing approaches are based on either anomaly detection or correlation analysis, performing not well in terms of accuracy or efficiency. To better understand the problem of root-cause metric identification, we conduct a preliminary study based on real-world data analysis and interactions with engineers. The key observation is that root-cause metrics should satisfy two requirements. One is that the metric is expected to behave abnormally during the incident; the other is that the anomaly pattern should meet physical meaning and engineers' demand. Motivated by the findings obtained from the study, we propose an effective approach named PatternMatcher to identifying root-cause metrics accurately. Specifically, PatternMatcher contains three steps, where coarse-grained anomaly detection aiming to filter out normal metrics, anomaly pattern classification aiming to filter out unimportant anomaly patterns, and root-cause metric ranking. An extensive study on four real-world datasets including 113 incident cases from a large commercial bank demonstrates that PatternMatcher outperforms all baseline approaches, achieving top-3 average accuracy of 0.91. Moreover, we have deployed PatternMatcher in practice and shared some successful cases from real deployment.

Microservice architecture is applied by an increasing number of systems because of its benefits on delivery, scalability, and autonomy. It is essential but challenging to localize root-cause microservices promptly when a fault occurs. Traces are helpful for root-cause microservice localization, and thus many recent approaches utilize them. However, these approaches are less practical due to relying on supervision or other unrealistic assumptions. To overcome their limitations, we propose a more practical root-cause microservice localization approach named TraceRCA. The key insight of TraceRCA is that a microservice with more abnormal and less normal traces passing through it is more likely to be the root cause. Based on it, TraceRCA is composed of trace anomaly detection, suspicious microservice set mining and microservice ranking. We conducted experiments on hundreds of injected faults in a widely-used open-source microservice benchmark and a production system. The results show that TraceRCA is effective in various situations. The top-1 accuracy of TraceRCA outperforms the state-of-the-art unsupervised approaches by 44.8%. Besides, TraceRCA is applied in a large commercial bank, and it helps operators localize root causes for real-world faults accurately and efficiently. We also share some lessons learned from our real-world deployment.

In large-scale online service systems, software changes are inevitable and frequent. Due to importing new code or configurations, changes are likely to incur incidents and destroy user experience. Thus it is essential for engineers to identify bad software changes, so as to reduce the influence of incidents and improve system re- liability. To better understand bad software changes, we perform the first empirical study based on large-scale real-world data from a large commercial bank. Our quantitative analyses indicate that about 50.4% of incidents are caused by bad changes, mainly be- cause of code defect, configuration error, resource contention, and software version. Besides, our qualitative analyses show that the current practice of detecting bad software changes performs not well to handle heterogeneous multi-source data involved in soft- ware changes. Based on the findings and motivation obtained from the empirical study, we propose a novel approach named SCWarn aiming to identify bad changes and produce interpretable alerts accurately and timely. The key idea of SCWarn is drawing support from multimodal learning to identify anomalies from heterogeneous multi-source data. An extensive study on two datasets with various bad software changes demonstrates our approach significantly outperforms all the compared approaches, achieving 0.95 F1-score on average and reducing MTTD (mean time to detect) by 20.4%∼60.7%. In particular, we shared some success stories and lessons learned from the practical usage.

Log data is an essential and valuable resource of online service systems, which records detailed information of system running status and user behavior. Log anomaly detection is vital for service reliability engineering, which has been extensively studied. However, we find that existing approaches suffer from several limitations when deploying them into practice, including 1) inability to deal with various logs and complex log abnormal patterns; 2) poor interpretability; 3) lack of domain knowledge. To help understand these practical challenges and investigate the practical performance of existing work quantitatively, we conduct the first empirical study and an experimental study based on large-scale real-world data. We find that logs with rich information indeed exhibit diverse abnormal patterns (e.g., keywords, template count, template sequence, variable value, and variable distribution). However, existing approaches fail to tackle such complex abnormal patterns, producing unsatisfactory performance. Motivated by obtained findings, we propose a generic log anomaly detection system named LogAD based on ensemble learning, which integrates multiple anomaly detection approaches and domain knowledge, so as to handle complex situations in practice. About the effectiveness of LogAD, the average F1-score achieves 0.83, outperforming all baselines. Besides, we also share some success cases and lessons learned during our study. To our best knowledge, we are the first to investigate practical log anomaly detection in the real world deeply. Our work is helpful for practitioners and researchers to apply log anomaly detection to practice to enhance service reliability.

To ensure quality of service and user experience, large Internet companies often monitor various Key Performance Indicators (KPIs) of their systems so that they can detect anomalies and identify failure in real time. However, due to a large number of various KPIs and the lack of high-quality labels, existing KPI anomaly detection approaches either perform well only on certain types of KPIs or consume excessive resources. Therefore, to realize generic and practical KPI anomaly detection in the real world, we propose a KPI anomaly detection framework named iRRCF-Active, which contains an unsupervised and white-box anomaly detector based on Robust Random Cut Forest (RRCF), and an active learning component. Specifically, we novelly propose an improved RRCF (iRRCF) algorithm to overcome the drawbacks of applying original RRCF in KPI anomaly detection. Besides, we also incorporate the idea of active learning to make our model benefit from high-quality labels given by experienced operators. We conduct extensive experiments on a large-scale public dataset and a private dataset collected from a large commercial bank. The experimental resulta demonstrate that iRRCF-Active performs better than existing traditional statistical methods, unsupervised learning methods and supervised learning methods. Besides, each component in iRRCF-Active has also been demonstrated to be effective and indispensable.

Microservice systems are typically fragile and failures are inevitable in them due to their complexity and large scale. However, it is challenging to localize the root-cause metric due to its complicated dependencies and the huge number of various metrics. Existing methods are based on either correlation between metrics or correlation between metrics and failures. All of them ignore the key data source in microservice, i.e., logs. In this paper, we propose a novel root-cause metric localization approach by incorporating log anomaly detection. Our approach is based on a key observation, the value of root-cause metric should be changed along with the change of the log anomaly score of the system caused by the failure. Specifically, our approach includes two components, collecting anomaly scores by log anomaly detection algorithm and identifying root-cause metric by robust correlation analysis with data augmentation. Experiments on an open-source benchmark microservice system have demonstrated our approach can identify root-cause metrics more accurately than existing methods and only require a short localization time. Therefore, our approach can assist engineers to save much effort in diagnosing and mitigating failures as soon as possible.

In large-scale online service system, to enhance the quality of services, engineers need to collect various monitoring data and write many rules to trigger alerts. However, the number of alerts is way more than what on-call engineers can properly investigate. Thus, in practice, alerts are classified into several priority levels using manual rules, and on-call engineers primarily focus on handling the alerts with the highest priority level (i.e., severe alerts). Unfortunately, due to the complex and dynamic nature of the online services, this rule-based approach results in missed severe alerts or wasted troubleshooting time on non-severe alerts. In this paper, we propose AlertRank, an automatic and adaptive framework for identifying severe alerts. Specifically, AlertRank extracts a set of powerful and interpretable features (textual and temporal alert features, univariate and multivariate anomaly features for monitoring metrics), adopts XGBoost ranking algorithm to identify the severe alerts out of all incoming alerts, and uses novel methods to obtain labels for both training and testing. Experiments on the datasets from a top global commercial bank demonstrate that AlertRank is effective and achieves the F1-score of 0.89 on average, outperforming all baselines. The feedback from practice shows AlertRank can significantly save the manual efforts for on-call engineers.

Incidents in online service systems could dramatically degrade system availability and destroy user experience. To guarantee service quality and reduce economic loss, it is essential to predict the occurrence of incidents in advance so that engineers can take some proactive actions to prevent them. In this work, we propose an effective and interpretable incident prediction approach, called eWarn, which utilizes historical data to forecast whether an incident will happen in the near future based on alert data in real time. More specifically, eWarn first extracts a set of effective features (including textual features and statistical features) to represent omen alert patterns via careful feature engineering. To reduce the influence of noisy alerts (that are not relevant to the occurrence of incidents), eWarn then incorporates the multi-instance learning formulation. Finally, eWarn builds a classification model via machine learning and generates an interpretable report about the prediction result via a state-of-the-art explanation technique (i.e., LIME). In this way, an early warning signal along with its interpretable report can be sent to engineers to facilitate their understanding and handling for the incoming incident. An extensive study on 11 real-world online service systems from a large commercial bank demonstrates the effectiveness of eWarn, outperforming state-of-the-art alert-based incident prediction approaches and the practice of incident prediction with alerts. In particular, we have applied eWarn to two large commercial banks in practice and shared some success stories and lessons learned from real deployment.

KPI (Key Performance Indicator) anomaly detection is critical for Internet-based services to ensure the quality and reliability. However, existing algorithms' performance in reality is far from satisfying due to the lack of sufficient KPI anomaly data to help train and evaluate these algorithms. In this paper, we argue that labeling overhead is the main hurdle to obtain such datasets. Thus, we novelly propose a semi-automatic labelling tool called Label-Less, which minimizes the labeling overhead in order to enable an ImageNet-like large-scale KPI anomaly dataset with high-quality ground truth. One novel technique in Label-Less is robust and rapid anomaly similarity search, which saves operators from scanning and checking the long KPIs back and forth for abnormal patterns or label consistency. In our evaluations using 30 real KPIs from a large Internet company, our anomaly similarity search achieves the best F-score of 0.95 on average, and a real-time per-KPI response time (less than 0.5 second). Overall, the feedback from deployment in practice shows that Label-Less can reduce operators' labeling overhead by more than 90%.

Key performance indicator (KPI) anomaly detection (AD) is critical to ensure service quality and reliability. Due to the effects of work days, off days, festivals, and business activities on user behavior, KPIs may exhibit different patterns within different days, which we call periodicity profiles of KPIs. However, existing KPI AD approaches have difficulties in adapting to diverse periodicity profiles due to the lack of generality. In this paper, we propose an automatic and generic framework called Period, which can accurately detect the periodicity profiles through daily subsequences clustering, and improve the performance of AD methods by robustly and automatically adapting to different periodicity profiles. In our evaluation using several real-world KPIs with different periodicity profiles from large Internet-based services, the clustering algorithm used to detect periodicity can achieve about 0.95 accuracy on average. More importantly, further evaluation on 56 KPIs shows that Period can significantly improve the best F-score of several widely used AD approaches by up to 0.66.

To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e.g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation. However, anomaly detection for these seasonal KPIs with various patterns and data quality has been a great challenge, especially without labels. In this paper, we proposed Donut, an unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our key techniques, Donut greatly outperforms a state-of-arts supervised ensemble approach and a baseline VAE approach, and its best F-scores range from 0.75 to 0.9 for the studied KPIs from a top global Internet company. We come up with a novel KDE interpretation of reconstruction for Donut, making it the first VAE-based anomaly detection algorithm with solid theoretical explanation.

In recent years, various data clustering algorithms have been proposed in the data mining and engineering communities. However, there are still drawbacks in traditional clustering methods which are worth to be further investigated, such as clustering for the high dimensional data, learning an ideal affinity matrix which optimally reveals the global data structure, discovering the intrinsic geometrical and discriminative properties of the data space, and reducing the noises influence brings by the complex data input. In this paper, we propose a novel clustering algorithm called robust dual clustering with adaptive manifold regularization (RDC), which simultaneously performs dual matrix factorization tasks with the target of an identical cluster indicator in both of the original and projected feature spaces, respectively. Among which, the$l_{2,1}$-norm is used instead of the conventional$l_{2}$-norm to measure the loss, which helps to improve the model robustness by relieving the influences by the noises and outliers. In order to better consider the intrinsic geometrical and discriminative data structure, we incorporate the manifold regularization term on the cluster indicator by using a particularly learned affinity matrix which is more suitable for the clustering task. Moreover, a novel augmented lagrangian method (ALM) based procedure is designed to effectively and efficiently seek the optimal solution of the proposed RDC optimization. Numerous experiments on the representative data sets demonstrate the superior performance of the proposed method compares to the existing clustering algorithms.

Gait recognition is a rising biometric technology which aims to distinguish people purely through the analysis of the way they walk, while the problem is that the dimensionality of the gait data is too high, so it is necessary to carry on dimensionality reduction task. Up to date, in the area of computer vision and pattern recognition, various dimensionality reduction algorithms have been employed for gait data, including the conventional vector representation based methods principal components analysis (PCA) and, locality preserving projection (LPP), and the recently proposed multi-linear subspace learning based approaches such as multilinear principal component analysis (MPCA). In this paper, inspired by the advantages of the tensor representation and manifold learning, we propose a novel sparse tensor discriminative locality alignment for human gait feature representation and dimensionality reduction algorithm, and subsequently apply the refined feature for gait recognition by a lazy classifier of the KNN. The proposed method adopts sparse multi-way projection based on the high-order version of discriminative locality alignment, by which the class separability is enhanced and the potential model overfitting is simultaneously avoided. Extensive experiments on the University of South Florida (USF) HumanID Gait Database show that the proposed method achieves better recognition rate compared with some existing classical dimensionality reduction algorithms.