# cloud-config.yaml
clouds:
  aliyun:
    provider: aliyun
    region: cn-hangzhou
    instance_type: ecs.g7.xlarge
    monthly_cost: 389
    status: active
    
  aws:
    provider: aws
    region: ap-southeast-1
    instance_type: t5.medium
    monthly_cost: 245
    status: active
    
  digitalocean:
    provider: digitalocean
    region: sgp1
    instance_type: c-2
    monthly_cost: 192
    status: active

# ai_scheduler/agent.py
import asyncio
import json
from typing import Dict, List, Optional
from langchain.agents import initialize_agent, AgentType
from langchain.chat_models import ChatOpenAI
from langchain.tools import Tool

class CloudResourceScheduler:
    """AI-Driven Multi-Cloud Resource Scheduler"""
    
    def __init__(self, llm_model="gpt-4o"):
        self.llm = ChatOpenAI(model=llm_model, temperature=0.1)
        self.clouds = {}
        self.metrics_history = []
        
    async def fetch_cloud_prices(self, cloud_name: str) -> Dict:
        """Fetch real-time pricing for a given cloud provider"""
        price_map = {
            "aliyun": self._fetch_aliyun_pricing,
            "aws": self._fetch_aws_pricing,
            "digitalocean": self._fetch_do_pricing,
        }
        if cloud_name in price_map:
            return await price_map[cloud_name]()
        raise ValueError(f"Unsupported cloud: {cloud_name}")
    
    async def _fetch_aliyun_pricing(self) -> Dict:
        """Fetch Alibaba Cloud pricing info"""
        return {
            "instances": [
                {"type": "ecs.g7.xlarge", "price_per_hour": 1.82, "currency": "CNY"},
                {"type": "ecs.c7.2xlarge", "price_per_hour": 2.95, "currency": "CNY"},
            ],
            "spot_discount": 0.65,  # 35% spot instance discount
        }
    
    async def _fetch_aws_pricing(self) -> Dict:
        """Fetch AWS pricing info"""
        return {
            "instances": [
                {"type": "t5.medium", "price_per_hour": 0.026, "currency": "USD"},
                {"type": "m6i.xlarge", "price_per_hour": 0.192, "currency": "USD"},
            ],
            "spot_discount": 0.70,
        }
    
    async def _fetch_do_pricing(self) -> Dict:
        """Fetch DigitalOcean pricing info"""
        return {
            "instances": [
                {"type": "c-2", "price_per_hour": 0.265, "currency": "USD"},
                {"type": "c-4", "price_per_hour": 0.53, "currency": "USD"},
            ],
            "spot_discount": 0.81,
        }
    
    async def predict_load(self, hours_ahead: int = 24) -> Dict:
        """Predict future workload"""
        # Simple linear regression + periodic patterns
        # In production, use Prophet or LSTM
        base_cpu = 35.0
        trend = 0.5  # daily growth trend
        daily_cycle = lambda h: 20 * abs((h % 24) - 12) / 12
        
        predictions = {}
        for h in range(hours_ahead):
            cpu_pct = min(100, base_cpu + trend * (h / 24) + daily_cycle(h))
            predictions[h] = {
                "cpu_percent": round(cpu_pct, 1),
                "memory_gb": round(cpu_pct * 0.064, 1),
                "bandwidth_mbps": round(cpu_pct * 0.1, 1),
            }
        return predictions
    
    def optimize_costs(self, predictions: Dict, prices: Dict[str, Dict]) -> Dict:
        """AI-driven cost optimization decision"""
        best_config = None
        min_cost = float('inf')
        
        for cloud_name, cloud_data in prices.items():
            for inst in cloud_data.get("instances", []):
                spot_price = inst["price_per_hour"] * (1 - cloud_data.get("spot_discount", 0))
                monthly_cost = spot_price * 24 * 30
                
                exchange_rate = 7.2 if inst["currency"] == "USD" else 1.0
                monthly_cny = monthly_cost * exchange_rate
                
                if monthly_cny < min_cost:
                    min_cost = monthly_cny
                    best_config = {
                        "cloud": cloud_name,
                        "instance_type": inst["type"],
                        "hourly_cost": spot_price,
                        "monthly_cost_cny": round(monthly_cny, 2),
                    }
        
        return best_config
    
    async def generate_report(self) -> str:
        """Generate AI operations daily report"""
        prices = {}
        for cloud in self.clouds.keys():
            prices[cloud] = await self.fetch_cloud_prices(cloud)
        
        predictions = await self.predict_load(24)
        optimization = self.optimize_costs(predictions, prices)
        
        prompt = f"""You are a professional IT operations consultant. Generate a concise AI scheduling daily report based on the following data:

Current multi-cloud configuration and pricing:
{json.dumps(prices, indent=2, ensure_ascii=False)}

Next 24-hour load forecast:
{json.dumps({k: v for k, v in list(predictions.items())[:5]}, indent=2, ensure_ascii=False)}

Cost optimization recommendation:
{json.dumps(optimization, indent=2, ensure_ascii=False)}

Please output in the following format:
1. 📊 Current cost overview
2. 🔮 Load trend prediction
3. 💡 Optimization suggestions
4. ⚠️ Risk alerts
"""
        
        response = self.llm.predict(prompt)
        return response

# crontab -e
# Check cloud prices every 15 minutes
*/15 * * * * /opt/ai-scheduler/bin/check-pricing.sh >> /var/log/scheduler.log 2>&1

# Generate load forecast every hour
0 * * * * /opt/ai-scheduler/bin/predict-load.sh >> /var/log/scheduler.log 2>&1

# Generate daily ops report at 9 AM
0 9 * * * /opt/ai-scheduler/bin/daily-report.sh >> /var/log/scheduler.log 2>&1

# Run deep cost optimization every Monday at 10 AM
0 10 * * 1 /opt/ai-scheduler/bin/weekly-optimize.sh >> /var/log/scheduler.log 2>&1

# migration_rules.py
"""Define auto-migration strategies and conditions"""

MIGRATION_RULES = {
    "cost_threshold": {
        "description": "Trigger migration when cost exceeds threshold",
        "max_monthly_cost_cny": 500,
        "action": "switch_to_cheapest_cloud",
    },
    "performance_threshold": {
        "description": "Trigger scaling when performance drops below SLA",
        "min_cpu_available": 20,
        "max_response_time_ms": 200,
        "action": "upgrade_instance_or_migrate",
    },
    "availability_threshold": {
        "description": "Trigger failover when availability drops below target",
        "target_uptime": 99.95,
        "max_downtime_minutes": 4,
        "action": "failover_to_backup_cloud",
    },
    "spot_interruption": {
        "description": "Handling strategy when spot instance is reclaimed",
        "warning_time_minutes": 2,
        "action": "graceful_migration_to_on_demand",
    },
}

# smart_reporter.py
from langchain.prompts import PromptTemplate

def generate_explanatory_report(decisions: dict) -> str:
    """Generate an explainable AI ops report"""
    
    template = PromptTemplate(
        input_variables=["cost_savings", "recommendations", "risks"],
        template="""
## 📊 Weekly AI Scheduling Report

### Cost Savings Summary
Total savings this week: ${cost_savings}, primarily from:
- Cross-cloud migration: Saved $XXX
- Spot instance utilization: Saved $XXX
- Idle resource cleanup: Saved $XXX

### Smart Decision Log
{recommendations}

### Risk Alerts
{risks}

### Next Week Forecast
Based on historical data and current trends, we expect:
- Load to peak on Wednesday
- Recommend pre-scaling or preparing elastic resources
- A cloud provider may adjust pricing, stay alert
        """
    )
    
    return template.format(**decisions)

## 📊 Weekly AI Scheduling Report

### Cost Savings Summary
Total savings this week: $62, primarily from:
- Cross-cloud migration: Saved $31
- Spot instance utilization: Saved $22
- Idle resource cleanup: Saved $9

### Smart Decision Log
- Mon 09:00: Detected abnormal storage cost growth on Alibaba Cloud, recommended migration to S3
- Wed 14:30: Auto-scaled to AWS during promotion, released after conclusion
- Fri 22:00: Identified 2 low-utilization dev machines, recommended consolidation

### Risk Alerts
- Tencent Cloud ap-sea region instances are in short supply, prepare backup plans
- A cloud provider is about to adjust pricing, expected increase 10%-15%

# Use Vault to manage cloud provider API keys
vault kv put secret/cloud/aliyun \
  access_key_id="AK..." \
  access_key_secret="SK..."
  
vault kv put secret/cloud/aws \
  aws_access_key_id="AKIA..." \
  aws_secret_access_key="wJalr..."

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "ec2:Describe*",
        "ec2:StartInstances",
        "ec2:StopInstances",
        "ec2:TerminateInstances",
        "ec2:RunInstances"
      ],
      "Resource": "*"
    }
  ]
}