Implement analytical analysis using Jackson's theorem for open networks

Jackson's Theorem Implementation:
- analytics/jackson.py: Complete Jackson theorem analyzer
  - Calculate effective arrival rates (λᵢ)
  - Calculate utilizations (ρᵢ = λᵢ/μᵢ)
  - Stability check (ρᵢ < 1 for all queues)
  - M/M/1 formulas: L = ρ/(1-ρ), W = L/λ
  - System-wide metrics using Little's Law
  - Handles both stable and unstable queues

Mathematical Formulas:
- Effective arrival rates for network topology
- Utilization: ρᵢ = λᵢ / μᵢ
- Average customers: Lᵢ = ρᵢ / (1 - ρᵢ)
- Average time: Wᵢ = Lᵢ / λᵢ (Little's Law)
- Average wait: Wq = W - 1/μ
- Total system: L_total = Σ Lᵢ, W_total = L_total / λ₀

Comparison Module:
- analytics/comparison.py: Compare analytical vs simulation
  - QueueComparison for per-queue metrics
  - NetworkComparison for system-wide comparison
  - Percentage difference calculations
  - Formatted comparison reports

Testing & Validation:
- tests/test_analytics.py: 7 tests validating Jackson's theorem
  - Simple M/M/1 queue (analytical vs theory)
  - Unstable queue detection (ρ > 1)
  - Network with multiple servers
  - Little's Law validation (L = λW)
  - Probability conservation
  - Effective arrival rates
  - Multi-server stability

Demo & Examples:
- demo_analytical.py: Compare analytical vs simulation for scenarios
  - Runs scenarios 1-3
  - Shows theoretical predictions
  - Shows simulation results
  - Detailed comparison tables
  - Percentage differences

Results:
- ✅ All 7 analytical tests pass
- ✅ Little's Law validated for all queues
- ✅ Stability detection working correctly
- ✅ Comparison reveals expected statistical variation

Observations:
- Analytical predictions are accurate for stable systems
- Simulation shows statistical variation (finite sample)
- Unstable queues detected correctly (ρ ≥ 1)
- Differences increase with higher utilization

Phase 4 Complete ✓
Next: Phase 5 - Backend API implementation

Implement event-driven discrete simulation for queueing networks

Core Components:
- events.py: Event and EventType for discrete event simulation
  - Priority queue ordering by time
  - ARRIVAL and SERVICE_END event types

- request.py: Request entity with full visit tracking
  - QueueVisit records for each queue visited
  - Automatic calculation of wait/service/system times
  - Journey tracking through network

- random_utils.py: Random number generation
  - Exponential distribution (inverse transform method)
  - Probabilistic choice for routing decisions
  - Seed control for reproducibility

- queues.py: M/M/1 Queue implementation
  - FIFO discipline with single server
  - Complete statistics collection (utilization, avg times)
  - Exponential service times

- router.py: Probabilistic routing logic
  - Route from coordinator: exit (p) or server (qi)
  - Route from server: always return to coordinator
  - Probability conservation validation

- simulation.py: Main event-driven simulator
  - Priority queue (heapq) for event scheduling
  - Warmup period to reach steady state
  - Complete statistics generation
  - Support for multiple servers

Testing:
- test_random_utils.py: Validate exponential distribution (mean, reproducibility)
- test_simulation.py: End-to-end simulation tests
  - Stable and unstable systems
  - Multiple servers
  - Reproducibility with seeds

Demo:
- demo_simulation.py: Scenario 1 demonstration (1 fast server)
- Theoretical vs simulation comparison
- All 14 tests passing ✓

Phase 2 Complete ✓
Next: Phase 3 - Statistics collection and multi-server scenarios