Atri Mandal


Leveraging the Power of Log Clustering Algorithms to Reduce Alert Noise in IT Operations

Submitted May 22, 2024

The ever-increasing volume of alerts generated by monitoring tools poses a significant challenge for IT Operations teams. A substantial portion of these alerts are duplicates or false positives, overwhelming ITOps practitioners and hindering the timely identification of critical issues. Traditional methods for managing alert floods, such as manual filtering, are ineffective, prone to human error, and often become stale as the alert rules and configurations evolve. Conversely, deep learning-based approaches offer high accuracy but come with substantial computational and infrastructure costs.
This paper presents a novel, lightweight, and cost-effective solution for reducing alert noise in IT Operations. Our approach leverages a combination of log clustering algorithms and semantic similarity techniques that incorporate online learning for adaptability. We exploit the inherent patterns within alerts, often triggered by tools monitoring interconnected systems, to group similar alerts into clusters. Our experimental results demonstrate the effectiveness of the proposed approach across three key metrics: a high matching rate (upwards of 90%) for incoming alerts, a significant reduction in alert noise (over 99%) for matched alerts along with a high throughput, exceeding 1000 alerts per second. To address potential storage concerns, we propose a single-signature representation for each cluster, preventing exponential growth in storage costs.
This research offers a practical and efficient solution for IT Operations teams to tackle alert fatigue, improve their ability to identify critical issues, and ultimately enhance overall system health.


What is the problem?

  • Define alert flooding and its causes
  • Explain how alert flooding impacts ITOps personnel and system reliability and leads to increased costs.

Why do we need Alert Grouping?

  • Introduce the concept of alert grouping as a method to manage alert floods.
  • Explain how grouping similar alerts reduces noise and improves manageability.

Leveraging Log Clustering and Semantic Similarity

  • Motivate the need for a log clustering algorithm to understand alert patterns
  • Introduce Drain (open source) and briefly explain the rationale behind selecting it for clustering alerts
  • Explain how Drain can be fine-tuned and augmented with semantic similarity based search to achieve better results with respect to cluster quality and matching rate.

System Architecture

  • Provide a high-level overview of the system, including both training and inference pipelines.
  • Briefly explain how the system learns from incoming alerts and refines its clustering over time.

Experimental Evaluation and Results

  • Present key metrics used for evaluation (viz., matching rate, noise reduction, throughput).
  • Summarize the experimental findings, highlighting the system’s effectiveness with respect to key metrics as well as storage cost.

Conclusion and Future Work

  • Briefly summarize the key takeaways and the value proposition of the solution.
  • Discuss potential areas for future research and development.


  • Some references to prior research in this area

Response to questions

Who is the audience for your session?
The target audience for the session are:

  • IT Operations practitioners e.g. SREs, Cloud Operations Specialists, IT infrastructure managers etc. who deal with the problem of alert noise regularly
  • AI/ML Researchers who are interested in learning about novel applications of AI/ML
  • AIOps and ITSM Product Managers
  • AIOps and ITSM Developers
  • Tech professionals with on-call experience or responsibilities who may be interested in optimizing on-call workflows.

What problem/pain are you trying to solve (for the audience)?
The session focuses on addressing the critical challenge of alert flooding in IT Operations. The high volume of operational alerts, often with duplicates and false positives, causes alert fatigue and makes timely identification of critical issues difficult. This results in elevated business costs because of resource wastage and potential downtime from delayed resolution.

How will participants benefit from your session? What are the practical and specific ways in which they will be able to apply the knowledge they gain, and beyond just general awareness.

  • Participants will gain valuable insights and practical knowledge on how to reduce alert noise and improve IT Operations efficiency.
  • They will learn about a novel application of pattern mining algorithms. The proposed approach offers broader applicability for tasks like clustering or segmentation in various applications.
  • Participants will also learn about an open source log clustering tool and how to configure and fine tune it for their specific needs.
  • Last but not the least, they will get some practical guidance on building scalable machine learning solutions with continuous learning capabilities


{Replace this with an explanation of the impact of your work within your organization.}


{{ gettext('Login to leave a comment') }}

{{ gettext('Post a comment…') }}
{{ gettext('New comment') }}
{{ formTitle }}

{{ errorMsg }}

{{ gettext('No comments posted yet') }}

Hybrid Access Ticket

Hosted by

All about data science and machine learning

Supported by

Gold Sponsor

Atlassian unleashes the potential of every team. Our agile & DevOps, IT service management and work management software helps teams organize, discuss, and compl

Silver Sponsor