Agent自动化运维：从Self-healing到Auto-scaling

┌──────────────────────────────────────────────────────────┐
│               Agent自动化运维体系                          │
│                                                          │
│  ┌────────────┐  ┌──────────┐  ┌──────────┐             │
│  │ 健康检测    │  │ 告警      │  │ 自愈       │             │
│  │ Health     │  │ Alerts   │  │ Self-    │             │
│  │ Check      │  │          │  │ healing  │             │
│  └─────┬──────┘  └────┬─────┘  └────┬─────┘             │
│        │               │              │                   │
│        ▼               ▼              ▼                   │
│  ┌─────────────────────────────────────────────┐         │
│  │        决策引擎 (Decision Engine)             │         │
│  │  - 规则引擎                                   │         │
│  │  - ML预测模型                                 │         │
│  │  - 执行计划生成                               │         │
│  └─────────────────┬───────────────────────────┘         │
│                    │                                      │
│        ┌───────────┼───────────────┐                     │
│        ▼           ▼               ▼                     │
│  ┌─────────┐ ┌──────────┐  ┌────────────┐               │
│  │Auto-    │ │Auto-     │  │Auto-       │               │
│  │Scaling  │ │Remediation│  │Configuration│               │
│  │扩缩容    │ │修复       │  │配置        │               │
│  └─────────┘ └──────────┘  └────────────┘               │
└──────────────────────────────────────────────────────────┘

class HealthChecker:
    """Agent系统健康检查"""
    
    CHECKS = {
        "llm_service": {
            "endpoint": "http://llm-service:8080/health",
            "timeout": 5,
            "expected_status": 200,
            "degraded_threshold_ms": 500,
        },
        "tool_services": {
            "endpoint": "http://tool-service:8080/health",
            "timeout": 3,
            "expected_status": 200,
        },
        "vector_db": {
            "endpoint": "http://qdrant:6333/health",
            "timeout": 5,
        },
        "redis": {
            "command": "PING",
            "expected_response": "PONG",
        }
    }
    
    async def run_all_checks(self) -> dict:
        """运行所有健康检查"""
        results = {}
        
        for name, check in self.CHECKS.items():
            try:
                result = await self._run_check(name, check)
                results[name] = result
            except Exception as e:
                results[name] = {
                    "status": "unhealthy",
                    "error": str(e),
                    "timestamp": datetime.now().isoformat()
                }
        
        # 计算整体健康分
        healthy_count = sum(
            1 for r in results.values() 
            if r["status"] == "healthy"
        )
        health_score = healthy_count / len(results)
        
        return {
            "overall_health": health_score,
            "status": "healthy" if health_score > 0.8 else "degraded",
            "checks": results,
            "timestamp": datetime.now().isoformat()
        }
    
    async def _run_check(self, name: str, check: dict) -> dict:
        """运行单项检查"""
        start = time.monotonic()
        
        if "endpoint" in check:
            async with httpx.AsyncClient(timeout=check["timeout"]) as client:
                response = await client.get(check["endpoint"])
                latency_ms = (time.monotonic() - start) * 1000
                
                if response.status_code != check["expected_status"]:
                    return {
                        "status": "unhealthy",
                        "actual_status": response.status_code,
                        "expected_status": check["expected_status"]
                    }
                
                status = "healthy"
                if latency_ms > check.get("degraded_threshold_ms", 1000):
                    status = "degraded"
                
                return {
                    "status": status,
                    "latency_ms": latency_ms,
                    "status_code": response.status_code
                }

class SelfHealingActions:
    """自愈动作库"""
    
    async def restart_service(self, service_name: str) -> dict:
        """重启服务"""
        logger.warning(f"Self-healing: restarting {service_name}")
        
        try:
            # 通过K8s API重启
            await self.k8s_client.restart_deployment(service_name)
            
            # 等待就绪
            await self._wait_for_ready(service_name, timeout=120)
            
            return {
                "action": "restart_service",
                "service": service_name,
                "success": True
            }
        except Exception as e:
            logger.error(f"Self-healing failed for {service_name}: {e}")
            return {
                "action": "restart_service",
                "service": service_name,
                "success": False,
                "error": str(e)
            }
    
    async def clear_cache(self, cache_type: str) -> dict:
        """清理缓存"""
        if cache_type == "redis":
            await self.redis_client.flushdb()
        elif cache_type == "vector":
            await self.vector_db.clear_cache()
        
        return {"action": "clear_cache", "type": cache_type, "success": True}
    
    async def scale_service(
        self, 
        service_name: str, 
        replicas: int
    ) -> dict:
        """扩缩容服务"""
        current = await self.k8s_client.get_deployment_replicas(service_name)
        
        if current == replicas:
            return {"action": "scale", "changed": False}
        
        await self.k8s_client.scale_deployment(service_name, replicas)
        
        return {
            "action": "scale",
            "service": service_name,
            "from": current,
            "to": replicas
        }
    
    async def failover_to_backup(self, service_name: str) -> dict:
        """故障转移到备用服务"""
        backup_config = self.backup_configs.get(service_name)
        if not backup_config:
            raise ValueError(f"No backup configured for {service_name}")
        
        # 切换流量到备用
        await self.traffic_router.switch_to_backup(
            service_name, 
            backup_config["endpoint"]
        )
        
        return {
            "action": "failover",
            "service": service_name,
            "backup": backup_config["name"]
        }

class PredictiveAutoScaler:
    """预测性自动扩缩容"""
    
    def __init__(self, k8s_client, metrics_client):
        self.k8s = k8s_client
        self.metrics = metrics_client
        self.prediction_model = None  # 加载训练好的预测模型
    
    async def run_scaling_loop(self):
        """扩缩容主循环"""
        while True:
            try:
                # 1. 收集当前指标
                current_metrics = await self._collect_metrics()
                
                # 2. 预测未来负载
                predicted_load = await self._predict_load(
                    current_metrics, 
                    horizon_minutes=15
                )
                
                # 3. 计算目标副本数
                target_replicas = self._calculate_target_replicas(
                    predicted_load
                )
                
                # 4. 执行扩缩容
                await self._apply_scaling(target_replicas)
                
                # 5. 等待下一次评估
                await asyncio.sleep(60)  # 每分钟评估一次
                
            except Exception as e:
                logger.error(f"Auto-scaling error: {e}")
                await asyncio.sleep(60)
    
    async def _predict_load(self, current: dict, horizon_minutes: int) -> dict:
        """预测未来负载"""
        # 使用历史数据 + 当前趋势预测
        if self.prediction_model:
            features = self._extract_prediction_features(current)
            prediction = await self.prediction_model.predict(features)
            return prediction
        else:
            # 简单线性回归预测
            trend = self._calculate_trend(current)
            predicted_qps = current["qps"] * (1 + trend * horizon_minutes / 60)
            return {"predicted_qps": max(0, predicted_qps)}
    
    def _calculate_target_replicas(self, predicted_load: dict) -> dict:
        """计算目标副本数"""
        targets = {}
        
        for service, config in self.service_configs.items():
            predicted_qps = predicted_load.get("predicted_qps", 0)
            
            # 每个副本能处理的QPS
            qps_per_replica = config.get("qps_per_replica", 10)
            
            # 目标副本数（加20%安全余量）
            target = int(predicted_qps / qps_per_replica * 1.2)
            
            # 应用上下限
            target = max(config["min_replicas"], target)
            target = min(config["max_replicas"], target)
            
            targets[service] = target
        
        return targets

class ScalingStrategy:
    """扩缩容策略"""
    
    STRATEGIES = {
        "conservative": {
            "scale_up_step": 1,      # 每次只加1个副本
            "scale_down_step": 1,    # 每次只减1个副本
            "scale_up_cooldown": 120, # 扩容冷却2分钟
            "scale_down_cooldown": 300,  # 缩容冷却5分钟
        },
        "aggressive": {
            "scale_up_step": 5,
            "scale_down_step": 2,
            "scale_up_cooldown": 30,
            "scale_down_cooldown": 180,
        },
        "predictive": {
            "lookahead_minutes": 15,
            "safety_margin": 0.3,  # 30%安全余量
        }
    }
    
    async def execute_scaling(
        self,
        service: str,
        current: int,
        target: int,
        strategy: str = "conservative"
    ):
        """执行扩缩容"""
        config = self.STRATEGIES[strategy]
        
        if target > current:
            # 扩容
            step = config["scale_up_step"]
            new_replicas = min(current + step, target)
            
            logger.info(
                f"Scaling up {service}: {current} -> {new_replicas}"
            )
            await self.k8s.scale_deployment(service, new_replicas)
            
        elif target < current:
            # 缩容——更保守
            step = config["scale_down_step"]
            new_replicas = max(current - step, target)
            
            # 检查是否有正在处理的请求
            active_requests = await self._get_active_requests(service)
            if active_requests > 0:
                logger.info(
                    f"Postponing scale down for {service}: "
                    f"{active_requests} active requests"
                )
                return
            
            logger.info(
                f"Scaling down {service}: {current} -> {new_replicas}"
            )
            await self.k8s.scale_deployment(service, new_replicas)

class FailurePredictor:
    """故障预测器"""
    
    async def predict_failures(self) -> list:
        """预测可能发生的故障"""
        predictions = []
        
        # 1. 基于指标的预测
        metrics_anomalies = await self._detect_metric_anomalies()
        for anomaly in metrics_anomalies:
            predictions.append({
                "type": "metric_anomaly",
                "service": anomaly["service"],
                "probability": anomaly["probability"],
                "description": anomaly["description"],
                "recommended_action": anomaly["action"]
            })
        
        # 2. 基于日志的预测
        log_patterns = await self._analyze_log_patterns()
        for pattern in log_patterns:
            if pattern["risk_score"] > 0.7:
                predictions.append({
                    "type": "log_pattern",
                    "pattern": pattern["pattern"],
                    "probability": pattern["risk_score"],
                    "description": f"Detected pattern: {pattern['description']}"
                })
        
        # 3. 基于依赖健康的预测
        dependency_health = await self._check_dependency_health()
        for dep in dependency_health:
            if dep["health_score"] < 0.5:
                predictions.append({
                    "type": "dependency",
                    "dependency": dep["name"],
                    "probability": 1 - dep["health_score"],
                    "description": f"Dependency {dep['name']} is unhealthy"
                })
        
        return sorted(predictions, key=lambda p: p["probability"], reverse=True)

class AIOpsEngine:
    """AIOps引擎"""
    
    async def analyze_incident(self, incident: dict) -> dict:
        """AI辅助事故分析"""
        
        # 1. 收集相关日志/指标/Trace
        context = await self._gather_incident_context(incident)
        
        # 2. 使用LLM分析根因
        analysis = await self.llm.analyze(
            prompt=f"""
            Analyze this incident and identify the root cause:
            
            Incident: {incident}
            Context: {context}
            
            Provide:
            1. Most likely root cause
            2. Contributing factors
            3. Recommended actions
            4. Prevention measures
            """
        )
        
        # 3. 从历史事故中找相似案例
        similar_incidents = await self._find_similar_incidents(incident)
        
        return {
            "incident_id": incident["id"],
            "root_cause_analysis": analysis["root_cause"],
            "recommended_actions": analysis["actions"],
            "similar_incidents": similar_incidents,
            "confidence": analysis["confidence"]
        }
    
    async def generate_runbook(self, incident_type: str) -> str:
        """自动生成Runbook"""
        return await self.llm.generate(
            prompt=f"""
            Generate a detailed runbook for handling {incident_type} incidents.
            Include:
            1. Detection steps
            2. Diagnosis procedures
            3. Resolution steps
            4. Verification steps
            5. Post-mortem template
            """
        )