Identifying recovery patterns from resource usage data of cluster systems

Failure of Cluster Systems has proven to be of adverse effect and it can be costly. System administrators have employed divide and conquer approach to diagnosing the root-cause of such failure in order to take corrective or preventive measures. Most times, event logs are the source of the information about the failures. Events that characterized failures are then noted and categorized as causes of failure. However, not all the ’causative’ events lead to eventual failure, as some faults sequence experience recovery. Such sequences or patterns constitute challenge to system administrators and failure prediction tools as they add to false positives. Their presence are always predicted as “failure causing“, while in reality, they will not. In order to detect such recovery patterns of events from failure patterns, we proposed a novel approach that utilizes resource usage data of cluster systems to identify recovery and failure sequences. We further propose an online detection approach to the same problem. We experiment our approach on data from Ranger Supercomputer System and the results are positive.Keywords: Change point detection; resource usage data; recovery sequence; detection; large-scale HPC system

Performance Analysis Tool for HPC and Big Data Applications on Scientific Clusters

Big data is prevalent in HPC computing. Many HPC projects rely on complex workflows to analyze terabytes or petabytes of data. These workflows often require running over thousands of CPU cores and performing simultaneous data accesses, data movements, and computation. It is challenging to analyze the performance involving terabytes or petabytes of workflow data or measurement data of the executions, from complex workflows over a large number of nodes and multiple parallel task executions. To help identify performance bottlenecks or debug the performance issues in large-scale scientific applications and scientific clusters, we have developed a performance analysis framework, using state-ofthe- art open-source big data processing tools. Our tool can ingest system logs and application performance measurements to extract key performance features, and apply the most sophisticated statistical tools and data mining methods on the performance data. It utilizes an efficient data processing engine to allow users to interactively analyze a large amount of different types of logs and measurements. To illustrate the functionality of the big data analysis framework, we conduct case studies on the workflows from an astronomy project known as the Palomar Transient Factory (PTF) and the job logs from the genome analysis scientific cluster

Linking resource usage anomalies with system failures from cluster log data

Bursts of abnormally high use of resources are thought to be an indirect cause of failures in large cluster systems, but little work has systematically investigated the role of high resource usage on system failures, largely due to the lack of a comprehensive resource monitoring tool which resolves resource use by job and node. The recently developed TACC_Stats resource use monitor provides the required resource use data. This paper presents the ANCOR diagnostics system that applies TACC_Stats data to identify resource use anomalies and applies log analysis to link resource use anomalies with system failures. Application of ANCOR to first identify multiple sources of resource anomalies on the Ranger supercomputer, then correlate them with failures recorded in the message logs and diagnosing the cause of the failures, has identified four new causes of compute node soft lockups. ANCOR can be adapted to any system that uses a resource use monitor which resolves resource use by job

Log-based software monitoring: a systematic mapping study

Modern software development and operations rely on monitoring to understand how systems behave in production. The data provided by application logs and runtime environment are essential to detect and diagnose undesired behavior and improve system reliability. However, despite the rich ecosystem around industry-ready log solutions, monitoring complex systems and getting insights from log data remains a challenge. Researchers and practitioners have been actively working to address several challenges related to logs, e.g., how to effectively provide better tooling support for logging decisions to developers, how to effectively process and store log data, and how to extract insights from log data. A holistic view of the research effort on logging practices and automated log analysis is key to provide directions and disseminate the state-of-the-art for technology transfer. In this paper, we study 108 papers (72 research track papers, 24 journals, and 12 industry track papers) from different communities (e.g., machine learning, software engineering, and systems) and structure the research field in light of the life-cycle of log data. Our analysis shows that (1) logging is challenging not only in open-source projects but also in industry, (2) machine learning is a promising approach to enable a contextual analysis of source code for log recommendation but further investigation is required to assess the usability of those tools in practice, (3) few studies approached efficient persistence of log data, and (4) there are open opportunities to analyze application logs and to evaluate state-of-the-art log analysis techniques in a DevOps context

Towards efficient error detection in large-scale HPC systems

The need for computer systems to be reliable has increasingly become important as the dependence on their accurate functioning by users increases. The failure of these systems could very costly in terms of time and money. In as much as system's designers try to design fault-free systems, it is practically impossible to have such systems as different factors could affect them. In order to achieve system's reliability, fault tolerance methods are usually deployed; these methods help the system to produce acceptable results even in the presence of faults. Root cause analysis, a dependability method for which the causes of failures are diagnosed for the purpose of correction or prevention of future occurrence is less efficient. It is reactive and would not prevent the first failure from occurring. For this reason, methods with predictive capabilities are preferred; failure prediction methods are employed to predict the potential failures to enable preventive measures to be applied. Most of the predictive methods have been supervised, requiring accurate knowledge of the system's failures, errors and faults. However, with changing system components and system updates, supervised methods are ineffective. Error detection methods allows error patterns to be detected early to enable preventive methods to be applied. Performing this detection in an unsupervised way could be more effective as changes to systems or updates would less affect such a solution. In this thesis, we introduced an unsupervised approach to detecting error patterns in a system using its data. More specifically, the thesis investigates the use of both event logs and resource utilization data to detect error patterns. It addresses both the spatial and temporal aspects of achieving system dependability. The proposed unsupervised error detection method has been applied on real data from two different production systems. The results are positive; showing average detection F-measure of about 75%

Logging Statements Analysis and Automation in Software Systems with Data Mining and Machine Learning Techniques

Log files are widely used to record runtime information of software systems, such as the timestamp of an event, the name or ID of the component that generated the log, and parts of the state of a task execution. The rich information of logs enables system developers (and operators) to monitor the runtime behavior of their systems and further track down system problems in development and production settings. With the ever-increasing scale and complexity of modern computing systems, the volume of logs is rapidly growing. For example, eBay reported that the rate of log generation on their servers is in the order of several petabytes per day in 2018 [17]. Therefore, the traditional way of log analysis that largely relies on manual inspection (e.g., searching for error/warning keywords or grep) has become an inefficient, a labor intensive, error-prone, and outdated task. The growth of the logs has initiated the emergence of automated tools and approaches for log mining and analysis. In parallel, the embedding of logging statements in the source code is a manual and error-prone task, and developers often might forget to add a logging statement in the software's source code. To address the logging challenge, many e orts have aimed to automate logging statements in the source code, and in addition, many tools have been proposed to perform large-scale log le analysis by use of machine learning and data mining techniques. However, the current logging process is yet mostly manual, and thus, proper placement and content of logging statements remain as challenges. To overcome these challenges, methods that aim to automate log placement and content prediction, i.e., `where and what to log', are of high interest. In addition, approaches that can automatically mine and extract insight from large-scale logs are also well sought after. Thus, in this research, we focus on predicting the log statements, and for this purpose, we perform an experimental study on open-source Java projects. We introduce a log-aware code-clone detection method to predict the location and description of logging statements. Additionally, we incorporate natural language processing (NLP) and deep learning methods to further enhance the performance of the log statements' description prediction. We also introduce deep learning based approaches for automated analysis of software logs. In particular, we analyze execution logs and extract natural language characteristics of logs to enable the application of natural language models for automated log le analysis. Then, we propose automated tools for analyzing log files and measuring the information gain from logs for different log analysis tasks such as anomaly detection. We then continue our NLP-enabled approach by leveraging the state-of-the-art language models, i.e., Transformers, to perform automated log parsing