feat: 대역폭 추정 및 DAU 표시 기능 추가

- 동시접속자 기반 월간 대역폭 자동 추정
- DAU(일일활성사용자) 추정치 표시 (동접 × 10-14)
- 대역폭 기반 Linode/Vultr 자동 선택 로직
- 비용 분석에 대역폭 비용 포함
- 지역 미선택시 서울/도쿄/오사카/싱가포르 기본 표시
- 지역별 서버 분리 표시 (GROUP BY instance + region)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>

SETUP.md

@@ -0,0 +1,132 @@
+# Server Recommendation System - Setup Guide
+
+## 1. Create Cloudflare Resources
+
+### Create D1 Database
+```bash
+# Create D1 database
+npx wrangler d1 create server-recommend-db
+
+# Copy the database_id from output and update wrangler.toml
+```
+
+### Create KV Namespace
+```bash
+# Create KV namespace for caching
+npx wrangler kv:namespace create CACHE
+
+# Copy the id from output and update wrangler.toml
+```
+
+## 2. Initialize Database Schema
+
+```bash
+# Execute schema.sql against your D1 database
+npx wrangler d1 execute server-recommend-db --file=schema.sql
+```
+
+## 3. Seed Sample Data (Optional)
+
+Create a file `seed.sql` with sample server data and run:
+```bash
+npx wrangler d1 execute server-recommend-db --file=seed.sql
+```
+
+## 4. Development
+
+```bash
+# Start local development server
+npm run dev
+
+# Type checking
+npm run typecheck
+```
+
+## 5. Deployment
+
+```bash
+# Deploy to Cloudflare Workers
+npm run deploy
+```
+
+## API Endpoints
+
+### Health Check
+```bash
+curl https://your-worker.workers.dev/api/health
+```
+
+### Get Servers
+```bash
+curl "https://your-worker.workers.dev/api/servers?minCpu=4&minMemory=8"
+```
+
+### Request Recommendations
+```bash
+curl -X POST https://your-worker.workers.dev/api/recommend \
+  -H "Content-Type: application/json" \
+  -d '{
+    "cpu_cores": 4,
+    "memory_gb": 8,
+    "storage_gb": 100,
+    "network_bandwidth_mbps": 1000,
+    "sla_requirement": 99.9,
+    "budget_monthly": 200,
+    "regions": ["us-east-1", "us-west-2"]
+  }'
+```
+
+## Environment Variables
+
+No environment variables needed - all bindings are configured in `wrangler.toml`:
+- `AI`: Workers AI binding (automatic)
+- `DB`: D1 Database binding
+- `CACHE`: KV namespace binding
+
+## Testing Workers AI Integration
+
+The worker uses `@cf/meta/llama-3.1-8b-instruct` model. Test with:
+
+```bash
+# Local development automatically uses Workers AI
+npm run dev
+
+# Make test request
+curl -X POST http://localhost:8787/api/recommend \
+  -H "Content-Type: application/json" \
+  -d '{
+    "cpu_cores": 2,
+    "memory_gb": 4,
+    "storage_gb": 50
+  }'
+```
+
+## Cache Strategy
+
+Recommendations are cached in KV for 1 hour based on request parameters:
+- Cache key format: `recommend:cpu:X|mem:Y|stor:Z|...`
+- TTL: 3600 seconds (1 hour)
+- Cache hit/miss logged in console
+
+## Error Handling
+
+All endpoints return proper HTTP status codes:
+- `200`: Success
+- `400`: Invalid request (validation error)
+- `404`: Endpoint not found
+- `500`: Internal server error
+
+CORS is enabled for all origins (`Access-Control-Allow-Origin: *`).
+
+## Monitoring
+
+Enable observability in `wrangler.toml`:
+```toml
+[observability]
+enabled = true
+```
+
+View logs:
+```bash
+npx wrangler tail
+```