Skill: Analyze Network Performance

Domain

telecommunications

Description

Analyzes telecommunications network performance metrics identifying bottlenecks, quality issues, and optimization opportunities.

Use Cases

•Network health monitoring
•QoS analysis
•Capacity planning
•Fault identification

Proprietary Business Rules

Rule 1: KPI Threshold Analysis

Network KPI evaluation against thresholds.

Rule 2: Trend Detection

Performance degradation trend identification.

Rule 3: Root Cause Analysis

Issue root cause determination.

Rule 4: Capacity Forecasting

Network capacity projection.

Input Parameters

•analysis_id (string): Analysis identifier
•network_metrics (list): Performance measurements
•topology_data (dict): Network topology
•traffic_data (list): Traffic patterns
•threshold_config (dict): Performance thresholds
•time_range (dict): Analysis period

Output

•health_score (float): Network health rating
•kpi_analysis (dict): KPI performance summary
•issues_detected (list): Performance issues
•trend_analysis (dict): Performance trends
•recommendations (list): Optimization actions

Implementation

The analysis logic is implemented in network_analyzer.py and references data from CSV files:

•performance_metrics.csv - Reference data
•application_requirements.csv - Reference data
•capacity_planning.csv - Reference data
•sla_definitions.csv - Reference data
•alert_thresholds.csv - Reference data
•baseline_deviation.csv - Reference data
•parameters.csv - Reference data.

Usage Example

python

from network_analyzer import analyze_network

result = analyze_network(
    analysis_id="NET-001",
    network_metrics=[{"metric": "latency_ms", "value": 25, "node": "router_1"}],
    topology_data={"nodes": 50, "links": 75, "type": "mesh"},
    traffic_data=[{"timestamp": "2025-12-15T10:00", "throughput_gbps": 8.5}],
    threshold_config={"latency_warning": 30, "latency_critical": 50},
    time_range={"start": "2025-12-15", "end": "2025-12-16"}
)

print(f"Network Health: {result['health_score']}")

Test Execution