Create enterprise_simulation.py

core/enterprise_simulation.py

@@ -0,0 +1,376 @@
+"""
+Enterprise Feature Simulation - Shows what ARF Enterprise adds on top of OSS
+Not real execution, but demonstrates the value proposition
+"""
+import asyncio
+import logging
+from typing import Dict, Any, List
+from datetime import datetime
+import random
+
+logger = logging.getLogger(__name__)
+
+# Trial license for demo
+DEMO_TRIAL_LICENSE = "ARF-TRIAL-DEMO-2026"
+
+class EnterpriseFeatureSimulation:
+    """
+    Simulates Enterprise features that would be available with arf_enterprise package
+    
+    Shows:
+    1. Novel execution protocols
+    2. Rollback guarantees
+    3. Deterministic confidence
+    4. Autonomous healing
+    5. Enhanced safety features
+    """
+    
+    def __init__(self):
+        self.enterprise_available = False
+        self.trial_license = DEMO_TRIAL_LICENSE
+        self._check_enterprise()
+    
+    def _check_enterprise(self):
+        """Check if enterprise package is available"""
+        try:
+            # Try to import real enterprise package
+            from arf_enterprise import (
+                create_enterprise_server,
+                EnterpriseLLMClient,
+                RollbackController,
+                ExecutionMode,
+                DeterministicConfidence,
+                NovelExecutionIntent,
+                get_novel_execution_capabilities
+            )
+            self.enterprise_available = True
+            logger.info("✅ Real ARF Enterprise package available")
+        except ImportError:
+            self.enterprise_available = False
+            logger.info("⚠️ ARF Enterprise package not available - using simulation")
+    
+    async def enhance_oss_analysis(self, oss_analysis: Dict[str, Any], scenario_name: str) -> Dict[str, Any]:
+        """
+        Enhance OSS analysis with Enterprise features
+        
+        Shows what Enterprise adds:
+        - Novel execution protocols
+        - Rollback guarantees
+        - Deterministic confidence
+        - Business impact analysis
+        """
+        logger.info(f"🏢 Enhancing OSS analysis with Enterprise features for: {scenario_name}")
+        
+        enhancement_start = datetime.now()
+        
+        try:
+            # Extract data from OSS analysis
+            oss_intent = oss_analysis.get("analysis", {}).get("decision", {})
+            similar_incidents = oss_analysis.get("analysis", {}).get("recall", [])
+            detection = oss_analysis.get("analysis", {}).get("detection", {})
+            
+            # 1. Apply deterministic confidence system (Enterprise feature)
+            deterministic_confidence = self._create_deterministic_confidence(
+                detection, similar_incidents, scenario_name
+            )
+            
+            # 2. Apply novel execution protocols (Enterprise feature)
+            novel_execution = self._apply_novel_execution_protocols(
+                oss_intent, deterministic_confidence, scenario_name
+            )
+            
+            # 3. Prepare rollback guarantees (Enterprise feature)
+            rollback_guarantees = await self._prepare_rollback_guarantees(
+                oss_intent, scenario_name
+            )
+            
+            # 4. Calculate enhanced business impact (Enterprise feature)
+            business_impact = self._calculate_enhanced_business_impact(
+                scenario_name, similar_incidents
+            )
+            
+            # 5. Determine execution mode capabilities
+            execution_capabilities = self._get_execution_capabilities()
+            
+            enhancement_time = (datetime.now() - enhancement_start).total_seconds() * 1000
+            
+            return {
+                "enterprise_available": self.enterprise_available,
+                "trial_license": self.trial_license if not self.enterprise_available else "Real License",
+                "enhancements": {
+                    "deterministic_confidence": deterministic_confidence,
+                    "novel_execution_protocols": novel_execution,
+                    "rollback_guarantees": rollback_guarantees,
+                    "business_impact_analysis": business_impact,
+                    "execution_capabilities": execution_capabilities
+                },
+                "value_proposition": [
+                    "✅ Autonomous execution with safety guarantees",
+                    "✅ Novel execution protocols for unprecedented incidents",
+                    "✅ Deterministic confidence scoring (not just ML probabilities)",
+                    "✅ Rollback guarantees for zero-downtime deployments",
+                    "✅ Business-aware impact analysis",
+                    "✅ Audit trail and compliance reporting",
+                    f"✅ Execution modes: {', '.join(execution_capabilities['modes'])}"
+                ],
+                "processing_time_ms": enhancement_time,
+                "requires_real_enterprise": not self.enterprise_available,
+                "upgrade_cta": "Contact sales@arf.dev for Enterprise trial" if not self.enterprise_available else None
+            }
+            
+        except Exception as e:
+            logger.error(f"Enterprise enhancement failed: {e}")
+            return {
+                "enterprise_available": self.enterprise_available,
+                "error": str(e),
+                "fallback_message": "OSS analysis complete. Enterprise features require arf_enterprise package."
+            }
+    
+    def _create_deterministic_confidence(self, detection: Dict, similar_incidents: List, scenario_name: str) -> Dict[str, Any]:
+        """Simulate deterministic confidence system (Enterprise feature)"""
+        detection_confidence = detection.get("confidence", 0.85)
+        
+        # Calculate pattern confidence from similar incidents
+        if similar_incidents:
+            pattern_confidence = sum([inc.get("similarity_score", 0.7) for inc in similar_incidents]) / len(similar_incidents)
+            success_rate = sum([1 for inc in similar_incidents if inc.get("success", False)]) / len(similar_incidents)
+        else:
+            pattern_confidence = 0.75
+            success_rate = 0.70
+        
+        # Scenario-specific adjustments
+        scenario_factors = {
+            "Cache Miss Storm": {"historical_pattern": 0.92, "current_metrics": 0.87, "system_state": 0.95},
+            "Database Connection Pool Exhaustion": {"historical_pattern": 0.88, "current_metrics": 0.82, "system_state": 0.90},
+            "Kubernetes Memory Leak": {"historical_pattern": 0.90, "current_metrics": 0.85, "system_state": 0.92},
+            "API Rate Limit Storm": {"historical_pattern": 0.85, "current_metrics": 0.88, "system_state": 0.87},
+            "Network Partition": {"historical_pattern": 0.93, "current_metrics": 0.90, "system_state": 0.96},
+            "Storage I/O Saturation": {"historical_pattern": 0.87, "current_metrics": 0.83, "system_state": 0.89}
+        }
+        
+        factors = scenario_factors.get(scenario_name, {"historical_pattern": 0.85, "current_metrics": 0.80, "system_state": 0.85})
+        
+        # Combine factors deterministically (not just ML probability)
+        business_context = 0.88  # Always consider business impact
+        safety_margin = 0.95  # Enterprise includes safety margins
+        
+        components = [
+            {"component": "historical_pattern", "value": factors["historical_pattern"], "weight": 0.25},
+            {"component": "current_metrics", "value": factors["current_metrics"], "weight": 0.25},
+            {"component": "system_state", "value": factors["system_state"], "weight": 0.20},
+            {"component": "detection_confidence", "value": detection_confidence, "weight": 0.15},
+            {"component": "business_context", "value": business_context, "weight": 0.10},
+            {"component": "safety_margin", "value": safety_margin, "weight": 0.05}
+        ]
+        
+        # Calculate weighted score
+        weighted_score = sum(c["value"] * c["weight"] for c in components)
+        
+        return {
+            "score": round(weighted_score, 3),
+            "components": components,
+            "deterministic": True,  # Enterprise feature: deterministic not probabilistic
+            "explainable": True,  # Enterprise feature: each component explained
+            "safety_margin_included": True
+        }
+    
+    def _apply_novel_execution_protocols(self, oss_intent: Dict, confidence: Dict, scenario_name: str) -> Dict[str, Any]:
+        """Apply novel execution protocols (Enterprise feature)"""
+        # Determine novelty level based on confidence and scenario
+        confidence_score = confidence.get("score", 0.85)
+        
+        if confidence_score >= 0.95:
+            novelty_level = "KNOWN_PATTERN"
+            risk_category = "LOW"
+            execution_approach = "autonomous_safe"
+        elif confidence_score >= 0.85:
+            novelty_level = "PARTIAL_MATCH"
+            risk_category = "MEDIUM"
+            execution_approach = "human_approval_required"
+        else:
+            novelty_level = "NOVEL_SCENARIO"
+            risk_category = "HIGH"
+            execution_approach = "enhanced_monitoring_first"
+        
+        return {
+            "novelty_level": novelty_level,
+            "risk_category": risk_category,
+            "execution_approach": execution_approach,
+            "protocols_applied": [
+                "deterministic_confidence_validation",
+                "blast_radius_containment",
+                "business_hour_compliance",
+                "rollback_preparation",
+                "circuit_breaker_setup"
+            ],
+            "enterprise_feature": True,
+            "requires_license": True
+        }
+    
+    async def _prepare_rollback_guarantees(self, oss_intent: Dict, scenario_name: str) -> Dict[str, Any]:
+        """Prepare rollback guarantees (Enterprise feature)"""
+        await asyncio.sleep(0.1)  # Simulate rollback preparation
+        
+        component = oss_intent.get("component", "unknown")
+        
+        return {
+            "rollback_prepared": True,
+            "state_id": f"state_{datetime.now().timestamp()}",
+            "guarantee": "STRONG",
+            "recovery_time_estimate": "45 seconds",
+            "snapshot_strategy": "incremental",
+            "verification_complete": True,
+            "rollback_scenarios": [
+                f"Restore {component} to previous state",
+                "Rollback configuration changes",
+                "Restore database connections",
+                "Reset circuit breakers"
+            ],
+            "enterprise_feature": True,
+            "requires_enterprise_server": True
+        }
+    
+    def _calculate_enhanced_business_impact(self, scenario_name: str, similar_incidents: List) -> Dict[str, Any]:
+        """Calculate enhanced business impact (Enterprise feature)"""
+        # Get average savings from similar incidents
+        if similar_incidents:
+            avg_savings = sum(inc.get("cost_savings", 5000) for inc in similar_incidents) / len(similar_incidents)
+            avg_resolution_time = 15  # minutes (average from similar incidents)
+        else:
+            avg_savings = 6500
+            avg_resolution_time = 20
+        
+        # Scenario-specific impacts
+        scenario_impacts = {
+            "Cache Miss Storm": {
+                "users_affected": 45000,
+                "revenue_risk_per_hour": 8500,
+                "recovery_time_manual": 45,
+                "recovery_time_arf": 12
+            },
+            "Database Connection Pool Exhaustion": {
+                "users_affected": 25000,
+                "revenue_risk_per_hour": 4200,
+                "recovery_time_manual": 35,
+                "recovery_time_arf": 15
+            },
+            "Kubernetes Memory Leak": {
+                "users_affected": 35000,
+                "revenue_risk_per_hour": 5500,
+                "recovery_time_manual": 40,
+                "recovery_time_arf": 18
+            },
+            "API Rate Limit Storm": {
+                "users_affected": 20000,
+                "revenue_risk_per_hour": 3800,
+                "recovery_time_manual": 25,
+                "recovery_time_arf": 8
+            },
+            "Network Partition": {
+                "users_affected": 75000,
+                "revenue_risk_per_hour": 12000,
+                "recovery_time_manual": 60,
+                "recovery_time_arf": 20
+            },
+            "Storage I/O Saturation": {
+                "users_affected": 30000,
+                "revenue_risk_per_hour": 6800,
+                "recovery_time_manual": 50,
+                "recovery_time_arf": 22
+            }
+        }
+        
+        impact = scenario_impacts.get(scenario_name, {
+            "users_affected": 30000,
+            "revenue_risk_per_hour": 5000,
+            "recovery_time_manual": 30,
+            "recovery_time_arf": 15
+        })
+        
+        # Calculate ARF benefits
+        time_saved = impact["recovery_time_manual"] - impact["recovery_time_arf"]
+        cost_saved_per_incident = (impact["revenue_risk_per_hour"] / 60) * time_saved
+        
+        return {
+            "scenario_specific": True,
+            "users_protected": impact["users_affected"],
+            "revenue_risk_per_hour": f"${impact['revenue_risk_per_hour']:,}",
+            "recovery_times": {
+                "manual": f"{impact['recovery_time_manual']} minutes",
+                "arf": f"{impact['recovery_time_arf']} minutes",
+                "time_saved": f"{time_saved} minutes",
+                "percent_faster": f"{int((time_saved / impact['recovery_time_manual']) * 100)}%"
+            },
+            "cost_analysis": {
+                "cost_saved_per_incident": f"${int(cost_saved_per_incident):,}",
+                "estimated_annual_savings": f"${int(cost_saved_per_incident * 15 * 12):,}",  # 15 incidents/month
+                "roi_multiplier": "5.2×",
+                "payback_months": "6.0"
+            },
+            "enterprise_feature": True,
+            "business_aware": True
+        }
+    
+    def _get_execution_capabilities(self) -> Dict[str, Any]:
+        """Get execution mode capabilities (Enterprise feature)"""
+        return {
+            "modes": ["advisory", "approval", "autonomous"],
+            "current_mode": "autonomous" if self.enterprise_available else "advisory",
+            "requires_enterprise": ["approval", "autonomous"],
+            "safety_guarantees": {
+                "rollback": "guaranteed" if self.enterprise_available else "not_available",
+                "circuit_breaker": "enabled" if self.enterprise_available else "disabled",
+                "blast_radius": "enforced" if self.enterprise_available else "advisory_only",
+                "business_hours": "enforced" if self.enterprise_available else "monitored"
+            }
+        }
+    
+    async def simulate_execution(self, scenario_name: str, mode: str = "autonomous") -> Dict[str, Any]:
+        """Simulate Enterprise execution"""
+        if mode == "advisory":
+            return {
+                "status": "advisory_only",
+                "message": "OSS mode: Execution not allowed. Upgrade to Enterprise for autonomous healing.",
+                "requires_enterprise": True,
+                "execution_mode": "advisory"
+            }
+        
+        await asyncio.sleep(0.3)
+        
+        if mode == "approval":
+            return {
+                "status": "awaiting_approval",
+                "message": "Enterprise Approval Mode: Healing intent created, awaiting human approval",
+                "requires_human_approval": True,
+                "estimated_savings": "$8,500",
+                "rollback_prepared": True,
+                "execution_mode": "approval"
+            }
+        else:  # autonomous
+            return {
+                "status": "executed",
+                "message": "Enterprise Autonomous Mode: Healing action executed with safety guarantees",
+                "execution_time": "12 minutes",
+                "cost_saved": "$8,500",
+                "rollback_available": True,
+                "rollback_guarantee": "STRONG",
+                "novel_execution_used": True,
+                "execution_mode": "autonomous",
+                "enterprise_features_used": [
+                    "deterministic_confidence",
+                    "novel_execution_protocols",
+                    "rollback_guarantees",
+                    "business_aware_execution"
+                ]
+            }
+
+
+# Factory function
+_enterprise_sim_instance = None
+
+async def get_enterprise_simulation() -> EnterpriseFeatureSimulation:
+    """Get singleton EnterpriseFeatureSimulation instance"""
+    global _enterprise_sim_instance
+    if _enterprise_sim_instance is None:
+        _enterprise_sim_instance = EnterpriseFeatureSimulation()
+    return _enterprise_sim_instance