Project Title: HPC Node/Cluster Failure Prediction With Prevention Using Logs and Monitoring Metrics

Problem Staement:- HPC cluster node failures lead to unexpected downtime, reducing system reliability and impacting performance.

Solution:- Developed an AI-powered predictive maintenance system that analyzes historical logs and real-time metrics (CPU usage, memory, network latency, disk health) using Prometheus and Grafana. Integrated machine learning models for anomaly detection and failure prediction, enabling proactive alerts and preventive actions, significantly reducing downtime and improving system stability.

Key Objectives

1. Proactive Maintenance – Identify potential failures before they occur to minimize downtime.
2. Log Analysis – Utilize historical log data to detect patterns that precede node or cluster failures.
3. Real-Time Monitoring – Incorporate system metrics such as CPU usage, memory consumption, network traffic, and disk health.
4. Machine Learning-Based Prediction – Develop models to classify or predict failure probabilities using supervised and unsupervised learning techniques.

Detailed Implementation Workflow

Step 1: Set Up the HPC Cluster

1. Hardware Setup:

• Master Node: Manages and schedules tasks.
• Compute Nodes: Execute computations.
• Networking: High-speed interconnects (InfiniBand, Gigabit Ethernet).
• Storage: Shared storage via NFS or parallel file systems like Lustre.

2. Software Stack:

• OS: Linux distributions optimized for HPC (CentOS, Ubuntu Server).
• Cluster Management: XCAT , OpenHPC or Rocks Cluster.
• Scheduler: SLURM, PBS, or Torque.
• MPI Library: OpenMPI or MPICH for parallel processing.

Step 2: Log Collection and Monitoring

1. Log Collection:

• Centralize logs with rsyslog or Fluentd.
• Collect logs from:
  • Job scheduler (e.g., SLURM logs).
  • System logs (/var/log/syslog, /var/log/messages).
  • Application logs.

2. Real-Time Monitoring:

• Prometheus: Master node metric collection.
• Node Exporter: Compute nodes hardware metrics.
• Key Metrics:
  • CPU/GPU utilization
  • Memory usage
  • Disk health/I/O
  • Network traffic
  • Errror Code
  • Warnings

Step 3: Data Aggregation and Preprocessing

1. Data Pipeline:

• Use Logstash or Fluentd to collect and preprocess logs.
• Store data in Elasticsearch or InfluxDB.

2. Data Preprocessing:

• Clean and parse logs.
• Extract time-series metrics.
• Annotate logs with failure events for supervised learning.

Step 4: Machine Learning for Failure Prediction

1. Feature Engineering:

• Extract features from logs:
  • Error codes, warning frequency.
  • Trends in CPU/GPU temperature, memory, disk I/O.
• Create time-series features from Prometheus metrics.

2. Model Training:

• Train models on historical logs and metrics:
  • Anomaly detection: Isolation Forest, Autoencoders.
  • Predictive modeling: XGBoost, Random Forest, LSTM.
• Develop models using Jupyter Notebooks or Python scripts.

3. Model Deployment:

• Export models with TensorFlow Serving or ONNX.
• Integrate with Grafana for real-time predictions.

Step 5: Integration and Alerting

1. Monitoring Dashboard:

• Grafana visualization:
  • Real-time metrics
  • Failure probability predictions

2. Alerting System:

• AlertManager: Alerts via email, Slack, or PagerDuty.
• Trigger alerts on high failure probability.

Step 6: Validation and Testing

• Simulate node failures (e.g., node overload).
• Validate model performance with Precision, Recall, F1 Score.

Tools Summary

Function	Tools
Cluster Management	XCAT , OpenHPC, SLURM, MPI
Monitoring	Prometheus, Node Exporter, Grafana
Log Analysis	Fluentd, Logstash, Elasticsearch
Modeling	Python (Scikit-learn, TensorFlow, PyTorch)
Deployment	Docker, Kubernetes

Expected Outcomes

✅ Real-Time Monitoring Dashboard – Displays node status and failure alerts.
✅ Predictive Failure Model – Provides failure probability and contributing factors.
✅ Reduced Downtime – Enables proactive system maintenance, enhancing overall reliability.