ข้อมูลเชิงลึก - Cloud Computing - # Incident Linking in Cloud Systems

Dependency Aware Incident Linking in Large Cloud Systems: Leveraging Textual and Graphical Data for Efficient Incident Management

Q: How can the proposed DiLink model be adapted for incident management in other cloud service providers?

The DiLink model can be adapted for incident management in other cloud service providers by following a similar approach to leverage both textual information and service dependency graph data. The key steps to adapt the model include: Data Collection: Gather incident data from the cloud service provider, including textual information such as incident titles, descriptions, severity, and impacted components. Additionally, collect service dependency information to build a dependency graph. Model Training: Train the DiLink model using the collected data, incorporating both the textual embeddings and the graph embeddings. Use techniques like Orthogonal Procrustes for aligning the embeddings from different modalities. Deployment: Deploy the trained model in the cloud service provider's infrastructure, utilizing platforms like Azure Machine Learning or similar services. Create an endpoint for real-time inference to predict related incident links. Integration with Incident Management System: Integrate the model predictions with the incident management system of the cloud service provider. Provide suggestions for incident links to On-call Engineers (OCEs) through channels like discussion sections, emails, or chatbots. Continuous Improvement: Gather feedback from OCEs on the accuracy of the predicted incident links and use this feedback to continuously improve the model's performance. By following these steps, the DiLink model can be effectively adapted for incident management in other cloud service providers, helping to streamline incident resolution and reduce manual toil.

Q: What are the potential drawbacks of relying heavily on automated incident linking models in large cloud systems?

While automated incident linking models like DiLink offer significant benefits in terms of efficiency and accuracy, there are potential drawbacks to relying heavily on these models in large cloud systems: Over-reliance on Machine Learning: Depending too much on automated models can lead to a reduction in human oversight and critical thinking. OCEs may become complacent and trust the model's predictions without verifying the incident links manually. Model Bias and Errors: Automated models are susceptible to biases present in the training data, which can result in incorrect incident links being predicted. Errors in the model can propagate and lead to cascading effects on incident resolution. Complexity and Interpretability: Automated models, especially those combining textual and graphical data, can be complex and challenging to interpret. Understanding the reasoning behind the model's predictions may be difficult, leading to potential misinterpretation of incident links. Dependency on Data Quality: The performance of automated incident linking models heavily relies on the quality and completeness of the data used for training. Inaccurate or incomplete data can lead to suboptimal model performance. Scalability and Maintenance: As cloud systems evolve and scale, maintaining and updating automated incident linking models to adapt to new services, dependencies, and incident patterns can be resource-intensive and time-consuming. Security and Privacy Concerns: Automated incident linking models may process sensitive data related to incidents and service dependencies. Ensuring the security and privacy of this data is crucial to prevent unauthorized access or misuse.

Q: How can the concept of incident linking be applied to improve incident response in non-cloud computing environments?

The concept of incident linking can be applied to improve incident response in non-cloud computing environments by enhancing the understanding of incident relationships and accelerating the resolution process. Here's how it can be implemented: Data Collection and Analysis: Gather incident data from various systems and applications in the non-cloud environment, including incident details, timestamps, and impacted components. Analyze this data to identify patterns and relationships between incidents. Model Development: Develop an incident linking model that incorporates textual information from incident descriptions and service dependencies. Utilize techniques like machine learning and graph analysis to predict related incident links accurately. Integration with Incident Management Systems: Integrate the incident linking model with the existing incident management system in the non-cloud environment. Provide OCEs with automated suggestions for related incidents to streamline the incident resolution process. Real-time Inference: Implement real-time inference capabilities to predict incident links as new incidents are reported. This enables quick identification of related incidents and helps OCEs prioritize and resolve issues efficiently. Feedback and Iteration: Gather feedback from OCEs on the accuracy of the predicted incident links and continuously refine the model based on this feedback. Regularly update the model to adapt to changing incident patterns and dependencies. By applying the concept of incident linking in non-cloud computing environments, organizations can enhance incident response capabilities, reduce manual effort in incident resolution, and improve overall system reliability.

แนวคิดหลัก

Efficiently linking incidents in large cloud systems by leveraging both textual and service dependency graph information is crucial for improving incident management and reducing manual toil.

บทคัดย่อ

Large-scale cloud services face production incidents impacting service availability and customer satisfaction.
Incident linking models are essential for grouping related incidents to mitigate major outages and reduce manual effort.
Dependency-aware incident linking framework proposed to improve accuracy and coverage of incident links.
Orthogonal Procrustes method used to align embeddings of textual and graphical data.
Experimental results show significant improvement in F1-score with the proposed DiLink model.
Real-world deployment process involves Azure machine learning platform and Azure Kubernetes cluster.
Link suggestions communicated to On-call Engineers through IcM Discussion, emails, and Teams chatbot.
Sensitivity analysis conducted on parameters like embedding size and number of neighbourhood hops.

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arxiv.org

สถิติ

Extensive experimental results on real-world incidents from 5 workloads of Microsoft demonstrate that our alignment method has an F1-score of 0.96 (14% gain over current state-of-the-art methods).
We generate more than 1 million triplets with an anchor incident and its corresponding related and non-related incidents using 9 months of historical data from 2022.

คำพูด

"Developing efficient incident linking models is of paramount importance for grouping related incidents into clusters so as to quickly resolve major outages and reduce on-call fatigue."
"Our alignment method has an F1-score of 0.96, a significant 14% gain over state-of-the-art methods."

ข้อมูลเชิงลึกที่สำคัญจาก

Dependency Aware Incident Linking in Large Cloud Systems

by Supriyo Ghos... ที่ arxiv.org 03-28-2024

https://arxiv.org/pdf/2403.18639.pdf

Dependency Aware Incident Linking in Large Cloud Systems

สอบถามเพิ่มเติม

How can the proposed DiLink model be adapted for incident management in other cloud service providers?

The DiLink model can be adapted for incident management in other cloud service providers by following a similar approach to leverage both textual information and service dependency graph data. The key steps to adapt the model include:

Data Collection: Gather incident data from the cloud service provider, including textual information such as incident titles, descriptions, severity, and impacted components. Additionally, collect service dependency information to build a dependency graph.

Model Training: Train the DiLink model using the collected data, incorporating both the textual embeddings and the graph embeddings. Use techniques like Orthogonal Procrustes for aligning the embeddings from different modalities.

Deployment: Deploy the trained model in the cloud service provider's infrastructure, utilizing platforms like Azure Machine Learning or similar services. Create an endpoint for real-time inference to predict related incident links.

Integration with Incident Management System: Integrate the model predictions with the incident management system of the cloud service provider. Provide suggestions for incident links to On-call Engineers (OCEs) through channels like discussion sections, emails, or chatbots.

Continuous Improvement: Gather feedback from OCEs on the accuracy of the predicted incident links and use this feedback to continuously improve the model's performance.

By following these steps, the DiLink model can be effectively adapted for incident management in other cloud service providers, helping to streamline incident resolution and reduce manual toil.

What are the potential drawbacks of relying heavily on automated incident linking models in large cloud systems?

While automated incident linking models like DiLink offer significant benefits in terms of efficiency and accuracy, there are potential drawbacks to relying heavily on these models in large cloud systems:

Over-reliance on Machine Learning: Depending too much on automated models can lead to a reduction in human oversight and critical thinking. OCEs may become complacent and trust the model's predictions without verifying the incident links manually.

Model Bias and Errors: Automated models are susceptible to biases present in the training data, which can result in incorrect incident links being predicted. Errors in the model can propagate and lead to cascading effects on incident resolution.

Complexity and Interpretability: Automated models, especially those combining textual and graphical data, can be complex and challenging to interpret. Understanding the reasoning behind the model's predictions may be difficult, leading to potential misinterpretation of incident links.

Dependency on Data Quality: The performance of automated incident linking models heavily relies on the quality and completeness of the data used for training. Inaccurate or incomplete data can lead to suboptimal model performance.

Scalability and Maintenance: As cloud systems evolve and scale, maintaining and updating automated incident linking models to adapt to new services, dependencies, and incident patterns can be resource-intensive and time-consuming.

Security and Privacy Concerns: Automated incident linking models may process sensitive data related to incidents and service dependencies. Ensuring the security and privacy of this data is crucial to prevent unauthorized access or misuse.

How can the concept of incident linking be applied to improve incident response in non-cloud computing environments?

The concept of incident linking can be applied to improve incident response in non-cloud computing environments by enhancing the understanding of incident relationships and accelerating the resolution process. Here's how it can be implemented:

Data Collection and Analysis: Gather incident data from various systems and applications in the non-cloud environment, including incident details, timestamps, and impacted components. Analyze this data to identify patterns and relationships between incidents.

Model Development: Develop an incident linking model that incorporates textual information from incident descriptions and service dependencies. Utilize techniques like machine learning and graph analysis to predict related incident links accurately.

Integration with Incident Management Systems: Integrate the incident linking model with the existing incident management system in the non-cloud environment. Provide OCEs with automated suggestions for related incidents to streamline the incident resolution process.

Real-time Inference: Implement real-time inference capabilities to predict incident links as new incidents are reported. This enables quick identification of related incidents and helps OCEs prioritize and resolve issues efficiently.

Feedback and Iteration: Gather feedback from OCEs on the accuracy of the predicted incident links and continuously refine the model based on this feedback. Regularly update the model to adapt to changing incident patterns and dependencies.

By applying the concept of incident linking in non-cloud computing environments, organizations can enhance incident response capabilities, reduce manual effort in incident resolution, and improve overall system reliability.