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Logs are essential for the maintenance of large software systems. Software engineers often analyze logs for debugging, root cause analysis, and anomaly detection tasks. Logs, however, are partly structured, making the extraction of useful ...

• We propose AML, a new metric for evaluating the performance of a log parser.
• AML uses the concepts of omission and commission to measure log parsing inaccuracies.
• AML was used to evaluate the accuracy of 14 parsers applied to eight ...

To assist IT service developers and operators in managing their increasingly complex service landscapes, there is a growing effort to leverage artificial intelligence in operations. To speed up troubleshooting, log anomaly detection has received ...

AIOps (Artificial Intelligence for IT Operations) solutions leverage the massive data produced during the operation of large-scale systems and machine learning models to assist software engineers in their system operations. As operation data produced in ...

Slow task detection is a critical problem in cloud operation and maintenance since it is highly related to user experience and can bring substantial liquidated damages. Most anomaly detection methods detect it from a single-task aspect. However, ...

Logs are ubiquitous digital footprints, playing an indispensable role in system diagnostics, security analysis, and performance optimization. The extraction of actionable insights from logs is critically dependent on the log parsing process, which ...

Log analysis is one of the main techniques that engineers use for troubleshooting large-scale software systems. Over the years, many supervised, semi-supervised, and unsupervised log analysis methods have been proposed to detect system anomalies by ...

Major cloud providers have employed advanced AI-based solutions like large language models to aid humans in identifying the root causes of cloud incidents. Even though AI-driven assistants are be- coming more common in the process of analyzing root ...

The growing complexity of cloud based software systems has resulted in incident management becoming an integral part of the software development lifecycle. Root cause analysis (RCA), a critical part of the incident management process, is a demanding task ...

In order to overcome the challenges of inefficiency, high error rate and poor scalability in the traditional operations model, and to ensure that the data cloud can provide efficient and stable quality of service, this study explores AIOps strategies for ...

In cybersecurity, live production data for predictive analysis pose a significant challenge due to the inherently secure nature of the domain. Although there are publicly available, synthesized, and artificially generated datasets, authentic ...

Continuous deployment has been practiced for many years by companies developing web‐ and cloud‐based applications. To succeed with continuous deployment, these companies have a strong collaboration culture between the operations and development ...

With continuous deployment, the complexity of software‐intensive embedded systems increases. Therefore, the efforts needed to support and service these systems increase. AIOps can be a critical enabler in managing the increasing complexity without ...

Anomaly detection techniques are essential in automating the monitoring of IT systems and operations. These techniques imply that machine learning algorithms are trained on operational data corresponding to a specific period of time and that they are ...

As IT environments evolve in both size and complexity, observability tools are needed to monitor their health. As the anomalous events are detected, alerts are generated, leading to alert notifications to the Site Reliability Engineers(SREs). However, ...

Anomaly detection is an essential activity for identifying abnormal behaviours in microservice-based systems. A common approach is to model the system behaviour during normal operation using either distributed traces or profiling metrics. The ...

• Combining distributed traces with profiling metrics enhances anomaly detection.
• Relying solely on a single metric is insufficient for studying system performance.
• Both linear & nonlinear metric relationships are vital for system ...

This paper presents a privacy-centric approach to log parsing, addressing the growing need for privacy compliance in log management. We propose a novel log parser that focuses on data minimization, a key principle in privacy protection. By integrating ...

Information Technology has become a critical component in various industries, leading to an increased focus on software maintenance and monitoring. With the complexities of modern software systems, traditional maintenance approaches have become ...

Data science projects represent a greater challenge than software engineering for organizations pursuing their adoption. The diverse stakeholders involved emphasize the need for a collaborative culture in organizations. This article aims to offer joint ...

Artificial Intelligence for IT Operations (AIOps) leverages AI approaches to handle the massive amount of data generated during the operations of software systems. Prior works have proposed various AIOps solutions to support different tasks in ...

The increasing structural and behavioural complexity of modern IT systems and environments (IT-Sys|Envs) calls for adopting automated fault anticipation and forecasting mechanisms to mitigate risks, limit system disturbances and damage, and improve ...

Multi-cloud computing is a vitally important topic from both busi-ness and technical perspectives since it guarantees resiliency, avail-ability, and security. Due to the vast number of configurations among cloud providers, it is quite challenging to ...