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RLM Monitoring & Analytics

Leverage Vantage License Manager's comprehensive monitoring capabilities to gain deep insights into your RLM license utilization across users, teams, departments, and clusters. This guide focuses on using Vantage's license usage dashboard to understand and optimize license utilization patterns.

Vantage License Usage Dashboard​

Real-Time License Monitoring​

The Vantage dashboard provides live monitoring of your RLM license infrastructure with immediate insights into usage patterns:

Live Status Overview​

# Enable real-time monitoring in Vantage
vantage licenses monitor rlm-server \
  --real-time \
  --refresh-interval 30s \
  --include-features STARCCM,NXCAE,LSDYNA

# View live status
vantage dashboard licenses --server rlm-server --live

Dashboard Features:

  • Real-time usage meters: Live percentage utilization for each license feature
  • Active sessions table: Current users, applications, and checkout times
  • Server health indicators: RLM server status, web interface availability, response times
  • Queue status: Users waiting for licenses with estimated wait times
  • License expiration alerts: Upcoming renewals and expired licenses

License Utilization Heatmap​

# Dashboard configuration for utilization heatmap
apiVersion: dashboard.vantage.com/v1
kind: Panel
metadata:
  name: rlm-utilization-heatmap
spec:
  title: "RLM License Utilization by Hour"
  type: heatmap
  timeRange: 7d
  queries:
  - query: |
      license_usage_percent{server="rlm-server",feature=~"STARCCM|NXCAE|LSDYNA"}
    groupBy: [hour, feature]
    aggregation: avg
  visualization:
    colorScheme: "green-red"
    thresholds:
      - value: 70
        color: yellow
      - value: 90
        color: red

User Analytics and Tracking​

Individual User Insights​

Vantage provides detailed analytics for understanding individual user behavior and license consumption patterns:

User License Consumption Dashboard​

# Configure user-specific monitoring
vantage analytics users configure \
  --server rlm-server \
  --track-session-duration \
  --track-peak-usage \
  --track-idle-time \
  --generate-efficiency-scores

# View user analytics
vantage dashboard users --server rlm-server \
  --metrics efficiency,utilization,cost \
  --time-range 30d

User Analytics Features:

  • Session duration tracking: How long users typically keep licenses checked out
  • Peak usage patterns: When individual users are most active
  • License hoarding detection: Users who checkout licenses but don't actively use them
  • Efficiency scoring: Productivity metrics based on actual license utilization
  • Cost attribution: Per-user license costs for chargeback purposes

User Behavior Analysis​

# User behavior dashboard configuration
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: rlm-user-behavior
spec:
  title: "RLM User Behavior Analysis"
  refresh: 5m
  panels:
  - title: "Top License Users"
    type: table
    targets:
    - query: |
        topk(10, 
          sum by (user) (
            license_checkout_duration{server="rlm-server"}
          )
        )
    columns:
    - name: "User"
      field: "user"
    - name: "Total Hours"
      field: "value"
    - name: "Avg Session"
      field: "avg_session"
    - name: "Efficiency Score"
      field: "efficiency"
      
  - title: "License Utilization by User"
    type: bargraph
    targets:
    - query: |
        license_active_time_percent{server="rlm-server"} 
        by (user, feature)
    legend: "{{user}} - {{feature}}"
    
  - title: "Session Duration Distribution"
    type: histogram
    targets:
    - query: |
        histogram_quantile(0.5, 
          rate(license_session_duration_bucket{server="rlm-server"}[1h])
        )

User Productivity Metrics​

# Example: User productivity analysis script
#!/usr/bin/env python3
# /opt/rlm/bin/user-productivity-analysis.py

import requests
import pandas as pd
from datetime import datetime, timedelta

class RLMUserAnalytics:
    def __init__(self, vantage_api_endpoint, api_key):
        self.api_endpoint = vantage_api_endpoint
        self.headers = {'Authorization': f'Bearer {api_key}'}
    
    def get_user_license_data(self, server, days=30):
        """Fetch user license data from Vantage API"""
        end_date = datetime.now()
        start_date = end_date - timedelta(days=days)
        
        params = {
            'server': server,
            'start_date': start_date.isoformat(),
            'end_date': end_date.isoformat(),
            'include_user_details': True
        }
        
        response = requests.get(
            f"{self.api_endpoint}/licenses/usage/users", 
            headers=self.headers, 
            params=params
        )
        return response.json()
    
    def calculate_efficiency_scores(self, usage_data):
        """Calculate user efficiency scores"""
        df = pd.DataFrame(usage_data['users'])
        
        # Calculate efficiency metrics
        df['active_ratio'] = df['active_time'] / df['checkout_time']
        df['session_efficiency'] = df['productive_time'] / df['total_session_time']
        df['license_waste'] = df['idle_time'] / df['checkout_time']
        
        # Composite efficiency score (0-100)
        df['efficiency_score'] = (
            (df['active_ratio'] * 0.4) + 
            (df['session_efficiency'] * 0.4) + 
            ((1 - df['license_waste']) * 0.2)
        ) * 100
        
        return df
    
    def generate_user_insights(self, server):
        """Generate comprehensive user insights"""
        data = self.get_user_license_data(server)
        efficiency_df = self.calculate_efficiency_scores(data)
        
        insights = {
            'top_users': efficiency_df.nlargest(10, 'checkout_time'),
            'most_efficient': efficiency_df.nlargest(10, 'efficiency_score'),
            'improvement_candidates': efficiency_df.nsmallest(10, 'efficiency_score'),
            'license_hoarders': efficiency_df.nlargest(10, 'license_waste'),
            'summary_stats': {
                'avg_efficiency': efficiency_df['efficiency_score'].mean(),
                'total_users': len(efficiency_df),
                'high_efficiency_users': len(efficiency_df[efficiency_df['efficiency_score'] > 80]),
                'low_efficiency_users': len(efficiency_df[efficiency_df['efficiency_score'] < 50])
            }
        }
        
        return insights

# Usage example
if __name__ == "__main__":
    analytics = RLMUserAnalytics(
        "https://api.vantage.com/v1", 
        "your-api-key"
    )
    insights = analytics.generate_user_insights("rlm-server")
    
    print("User License Analytics Summary:")
    print(f"Average Efficiency Score: {insights['summary_stats']['avg_efficiency']:.1f}%")
    print(f"High Efficiency Users: {insights['summary_stats']['high_efficiency_users']}")
    print(f"Users Needing Improvement: {insights['summary_stats']['low_efficiency_users']}")

Team and Department Analytics​

Organizational License Distribution​

Vantage provides comprehensive analytics for understanding license utilization across organizational structures:

Department-Level Dashboard​

# Configure department tracking
vantage analytics departments configure \
  --server rlm-server \
  --map-users-to-departments /etc/vantage/user-department-mapping.yaml \
  --enable-cost-center-attribution \
  --enable-budget-tracking

# View department analytics
vantage dashboard departments --server rlm-server \
  --include-cost-analysis \
  --include-efficiency-metrics

Department Analytics Features:

  • Cost center attribution: Automatic allocation of license costs to departments
  • Budget tracking: Monitor department license spending against allocated budgets
  • Efficiency comparisons: Compare license utilization efficiency across departments
  • Peak usage analysis: Identify when different departments have highest license demands
  • Trend analysis: Track departmental usage growth or reduction over time

Team Collaboration Analysis​

# Team analytics dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: rlm-team-analytics
spec:
  title: "RLM Team License Analytics"
  panels:
  - title: "License Usage by Department"
    type: piechart
    targets:
    - query: |
        sum by (department) (
          license_usage_hours{server="rlm-server"}
        )
    
  - title: "Department Efficiency Scores"
    type: bargraph
    targets:
    - query: |
        avg by (department) (
          license_efficiency_score{server="rlm-server"}
        )
    thresholds:
    - value: 70
      color: green
    - value: 50
      color: yellow
    - value: 30
      color: red
    
  - title: "Peak Usage by Department"
    type: timeseries
    targets:
    - query: |
        max by (department) (
          license_concurrent_usage{server="rlm-server"}
        ) over_time[24h]
    legend: "{{department}}"
    
  - title: "License Cost Attribution"
    type: table
    targets:
    - query: |
        sum by (department, cost_center) (
          license_cost_dollars{server="rlm-server"}
        )
    columns:
    - name: "Department"
    - name: "Cost Center" 
    - name: "Monthly Cost"
    - name: "Per-Hour Cost"
    - name: "Efficiency %"

Cross-Department License Sharing​

# Configure license sharing analytics
vantage analytics sharing configure \
  --server rlm-server \
  --track-cross-department-usage \
  --identify-sharing-opportunities \
  --generate-optimization-recommendations

# View sharing opportunities
vantage analytics sharing-opportunities \
  --server rlm-server \
  --potential-savings \
  --recommended-pooling

Cluster-Based Analytics​

License Distribution Across Compute Resources​

Understand how RLM licenses are utilized across different compute clusters and resources:

Cluster Utilization Dashboard​

# Configure cluster-based tracking
vantage analytics clusters configure \
  --server rlm-server \
  --track-license-locality \
  --monitor-cluster-efficiency \
  --enable-geographic-distribution

# View cluster analytics
vantage dashboard clusters --server rlm-server \
  --include-geographic-view \
  --include-performance-metrics

Cluster Analytics Features:

  • Geographic distribution: License usage across different data centers and regions
  • Cluster efficiency: How effectively different clusters utilize checked-out licenses
  • Resource correlation: Relationship between compute resources and license consumption
  • Network impact: Latency and connectivity effects on license performance
  • Load balancing: Optimize license server placement for cluster access

Multi-Cluster License Optimization​

# Cluster optimization dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: rlm-cluster-optimization
spec:
  title: "RLM Multi-Cluster License Optimization"
  panels:
  - title: "License Usage by Cluster"
    type: geomap
    targets:
    - query: |
        license_usage_by_cluster{server="rlm-server"}
    visualization:
      mapType: "world"
      colorScheme: "green-yellow-red"
    
  - title: "Cluster License Efficiency"
    type: stat
    targets:
    - query: |
        avg by (cluster) (
          license_active_time_percent{server="rlm-server"}
        )
    thresholds:
    - value: 80
      color: green
    - value: 60
      color: yellow
    - value: 40
      color: red
    
  - title: "License Server Response Time by Cluster"
    type: timeseries
    targets:
    - query: |
        license_checkout_response_time{server="rlm-server"}
        by (cluster)
    legend: "{{cluster}}"
    unit: "ms"
    
  - title: "Optimal License Server Placement"
    type: table
    targets:
    - query: |
        license_optimization_recommendations{server="rlm-server"}
    columns:
    - name: "Cluster"
    - name: "Current Latency"
    - name: "Recommended Server"
    - name: "Potential Improvement"

Resource Allocation Optimization​

# Cluster license optimization script
#!/usr/bin/env python3
# /opt/rlm/bin/cluster-optimization.py

import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from geopy.distance import geodesic

class ClusterLicenseOptimizer:
    def __init__(self, vantage_api):
        self.api = vantage_api
    
    def analyze_cluster_usage(self, server, days=30):
        """Analyze license usage patterns across clusters"""
        data = self.api.get_cluster_usage_data(server, days)
        df = pd.DataFrame(data)
        
        # Calculate key metrics per cluster
        cluster_metrics = df.groupby('cluster').agg({
            'license_checkouts': 'sum',
            'active_time': 'mean',
            'response_time': 'mean',
            'geographic_lat': 'first',
            'geographic_lon': 'first'
        }).reset_index()
        
        return cluster_metrics
    
    def optimize_license_server_placement(self, cluster_metrics):
        """Optimize license server placement using k-means clustering"""
        # Extract geographic coordinates
        coords = cluster_metrics[['geographic_lat', 'geographic_lon']].values
        
        # Weight by usage
        weights = cluster_metrics['license_checkouts'].values
        
        # Find optimal server locations
        kmeans = KMeans(n_clusters=3, random_state=42)
        server_locations = kmeans.fit(coords, sample_weight=weights).cluster_centers_
        
        # Calculate improvements
        improvements = []
        for i, cluster in cluster_metrics.iterrows():
            current_distance = cluster['response_time']  # Proxy for distance
            
            # Find closest optimal server
            distances = [
                geodesic(
                    (cluster['geographic_lat'], cluster['geographic_lon']),
                    (server_lat, server_lon)
                ).kilometers
                for server_lat, server_lon in server_locations
            ]
            
            optimal_distance = min(distances)
            improvement = max(0, (current_distance - optimal_distance) / current_distance * 100)
            
            improvements.append({
                'cluster': cluster['cluster'],
                'current_response_time': current_distance,
                'optimal_server_lat': server_locations[np.argmin(distances)][0],
                'optimal_server_lon': server_locations[np.argmin(distances)][1],
                'potential_improvement': improvement
            })
        
        return improvements
    
    def generate_optimization_report(self, server):
        """Generate comprehensive optimization report"""
        cluster_metrics = self.analyze_cluster_usage(server)
        optimizations = self.optimize_license_server_placement(cluster_metrics)
        
        report = {
            'summary': {
                'total_clusters': len(cluster_metrics),
                'avg_response_time': cluster_metrics['response_time'].mean(),
                'total_checkouts': cluster_metrics['license_checkouts'].sum(),
                'potential_improvement': np.mean([opt['potential_improvement'] for opt in optimizations])
            },
            'cluster_analysis': cluster_metrics.to_dict('records'),
            'optimization_recommendations': optimizations
        }
        
        return report

Long-Term Usage Analysis​

# Configure historical trend analysis
vantage analytics trends configure \
  --server rlm-server \
  --retention-period 2y \
  --enable-seasonal-analysis \
  --enable-growth-prediction \
  --enable-capacity-planning

# Generate trend analysis
vantage analytics trends generate \
  --server rlm-server \
  --include-seasonal-patterns \
  --include-growth-projections \
  --include-capacity-recommendations

Seasonal Usage Patterns​

# Seasonal analysis dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: rlm-seasonal-analysis
spec:
  title: "RLM Seasonal Usage Patterns"
  panels:
  - title: "Monthly Usage Trends"
    type: timeseries
    timeRange: 2y
    targets:
    - query: |
        avg_over_time(
          license_usage_percent{server="rlm-server"}[30d]
        )
    annotations:
    - query: "holiday_periods"
      color: "red"
    - query: "academic_calendar_events"
      color: "blue"
    
  - title: "Day of Week Usage Patterns"
    type: heatmap
    targets:
    - query: |
        avg by (day_of_week, hour) (
          license_usage{server="rlm-server"}
        )
    visualization:
      xAxis: "hour"
      yAxis: "day_of_week"
    
  - title: "Peak Usage Predictions"
    type: graph
    targets:
    - query: |
        predict_linear(
          license_usage_max{server="rlm-server"}[6w], 4w
        )
    - query: |
        license_capacity{server="rlm-server"}
    legend:
    - "Predicted Peak Usage"
    - "Current Capacity"
    
  - title: "Capacity Planning Recommendations"
    type: stat
    targets:
    - query: |
        (
          predict_linear(license_usage_max{server="rlm-server"}[6w], 12w) 
          / license_capacity{server="rlm-server"}
        ) * 100
    thresholds:
    - value: 80
      color: yellow
    - value: 95
      color: red
    unit: "percent"
    title: "Predicted Capacity Usage (3 months)"

Cost Analysis and Optimization​

License Cost Attribution​

# Configure cost analysis
vantage analytics costs configure \
  --server rlm-server \
  --license-costs /etc/vantage/rlm-license-costs.yaml \
  --enable-roi-analysis \
  --enable-optimization-recommendations

# Generate cost analysis
vantage analytics costs analyze \
  --server rlm-server \
  --include-utilization-costs \
  --include-waste-analysis \
  --include-optimization-savings

ROI and Efficiency Metrics​

# Cost optimization dashboard
apiVersion: dashboard.vantage.com/v1
kind: Dashboard
metadata:
  name: rlm-cost-optimization
spec:
  title: "RLM Cost Analysis and Optimization"
  panels:
  - title: "License Cost vs Utilization"
    type: scatter
    targets:
    - query: |
        license_annual_cost{server="rlm-server"} 
        vs 
        license_utilization_percent{server="rlm-server"}
    visualization:
      xAxis: "Utilization %"
      yAxis: "Annual Cost $"
      bubbleSize: "user_count"
    
  - title: "Underutilized License Value"
    type: stat
    targets:
    - query: |
        sum(
          license_annual_cost{server="rlm-server"} 
          * (1 - license_utilization_percent{server="rlm-server"} / 100)
        )
    unit: "currency"
    title: "Annual Waste ($)"
    
  - title: "Cost per Productive Hour"
    type: table
    targets:
    - query: |
        license_annual_cost{server="rlm-server"} 
        / (license_productive_hours{server="rlm-server"} * 52)
    columns:
    - name: "License Feature"
    - name: "Annual Cost"
    - name: "Productive Hours/Week"
    - name: "Cost per Hour"
    - name: "ROI Score"
    
  - title: "Optimization Opportunities"
    type: bargraph
    targets:
    - query: |
        license_optimization_savings{server="rlm-server"}
        by (optimization_type)
    legend: "{{optimization_type}}"

Alerting and Notifications​

Intelligent Alert Configuration​

# Configure advanced alerting
vantage alerts configure rlm-server \
  --high-utilization-threshold 85 \
  --license-waste-threshold 30 \
  --server-response-time-threshold 5s \
  --unusual-usage-detection \
  --cost-budget-alerts

# Set up notification channels
vantage alerts channels configure \
  --email admin@vantage.com \
  --slack "#licenses" \
  --webhook https://alerts.vantage.com/rlm \
  --escalation-policy critical-licenses

Custom Alert Rules​

# Advanced alerting configuration
apiVersion: alerting.vantage.com/v1
kind: AlertRule
metadata:
  name: rlm-intelligent-alerts
spec:
  server: rlm-server
  rules:
  - name: "High Utilization Warning"
    condition: |
      license_usage_percent > 85
    duration: 5m
    severity: warning
    message: "RLM {{$labels.feature}} utilization is {{$value}}%"
    
  - name: "License Waste Alert"
    condition: |
      license_idle_time_percent > 30
    duration: 15m
    severity: warning
    message: "License waste detected: {{$labels.user}} has {{$labels.feature}} idle for {{$value}}%"
    
  - name: "Unusual Usage Pattern"
    condition: |
      abs(license_usage - license_usage_baseline) > (2 * license_usage_stddev)
    duration: 10m
    severity: info
    message: "Unusual usage pattern detected for {{$labels.feature}}"
    
  - name: "Cost Budget Exceeded"
    condition: |
      license_monthly_cost > license_monthly_budget
    duration: 1m
    severity: critical
    message: "License costs exceeded budget: ${{$value}} > ${{$labels.budget}}"
    
  - name: "Server Performance Degradation"
    condition: |
      license_checkout_response_time > 5000
    duration: 2m
    severity: warning
    message: "RLM server response time degraded: {{$value}}ms"

Integration with Business Systems​

ERP and Finance Integration​

# Business system integration script
#!/usr/bin/env python3
# /opt/rlm/bin/business-integration.py

import requests
import json
from datetime import datetime, timedelta

class BusinessSystemIntegrator:
    def __init__(self, vantage_api, erp_api, finance_api):
        self.vantage = vantage_api
        self.erp = erp_api
        self.finance = finance_api
    
    def sync_license_costs_to_finance(self, server):
        """Sync license usage costs to finance system"""
        # Get usage data from Vantage
        usage_data = self.vantage.get_cost_data(server)
        
        # Transform for finance system
        finance_entries = []
        for dept_usage in usage_data['departments']:
            entry = {
                'cost_center': dept_usage['cost_center'],
                'account_code': 'LICENSE_SOFTWARE',
                'amount': dept_usage['monthly_cost'],
                'description': f"RLM licenses - {dept_usage['department']}",
                'period': datetime.now().strftime('%Y-%m'),
                'category': 'software_licensing'
            }
            finance_entries.append(entry)
        
        # Submit to finance system
        return self.finance.submit_cost_allocation(finance_entries)
    
    def update_project_budgets(self, server):
        """Update project budgets based on license usage"""
        project_usage = self.vantage.get_project_usage(server)
        
        for project in project_usage['projects']:
            budget_update = {
                'project_id': project['project_id'],
                'license_cost_actual': project['actual_cost'],
                'license_cost_projected': project['projected_cost'],
                'utilization_efficiency': project['efficiency_score']
            }
            
            self.erp.update_project_budget(budget_update)
    
    def generate_executive_report(self, server):
        """Generate executive-level license report"""
        metrics = self.vantage.get_executive_metrics(server)
        
        report = {
            'period': datetime.now().strftime('%Y-%m'),
            'total_license_value': metrics['total_annual_cost'],
            'utilization_efficiency': metrics['avg_efficiency'],
            'cost_per_productive_hour': metrics['cost_per_hour'],
            'optimization_opportunities': metrics['savings_potential'],
            'user_satisfaction_score': metrics['user_satisfaction'],
            'compliance_status': 'compliant',
            'recommendations': metrics['executive_recommendations']
        }
        
        return report

API Integration and Custom Reporting​

Custom Analytics API​

# Create custom analytics queries
vantage api analytics create-query \
  --name "weekly-rlm-efficiency" \
  --server rlm-server \
  --query "avg(license_efficiency_score) by (week)" \
  --schedule "0 9 * * MON" \
  --output-format json \
  --webhook https://internal.company.com/license-reports

# Generate custom reports
vantage api reports generate \
  --template executive-license-summary \
  --server rlm-server \
  --recipients "executives@company.com" \
  --schedule monthly

Next Steps​

Analytics Best Practice: Regular review of license analytics helps identify optimization opportunities and ensures maximum ROI from your software investments. Set up automated reports to stakeholders and establish monthly license review meetings.