import { logger } from '../../utils/logger';

export interface InvocationRecord {
  functionName: string;
  timestamp: number;
  durationMs: number;
}

export interface PredictionResult {
  functionName: string;
  nextPeakTimestamp: number;
  confidence: number;
  suggestedWarmupCount: number;
}

/**
 * [CORE_DEV_19] 无服务器冷启动延迟预测优化 (Cold-Start Predictor)
 * @description 利用时间序列分析预测云函数 (Edge Functions) 的调用高峰，提前预热容器以消除冷启动延迟。
 */
export class ColdStartPredictorService {
  private static history: Map<string, InvocationRecord[]> = new Map();

  /**
   * 记录函数调用历史
   */
  static recordInvocation(record: InvocationRecord) {
    const records = this.history.get(record.functionName) || [];
    records.push(record);
    if (records.length > 500) records.shift(); // 仅保留最近 500 条
    this.history.set(record.functionName, records);
  }

  /**
   * 预测下一次高峰并生成预热建议
   */
  static async predictNextPeak(functionName: string): Promise<PredictionResult | null> {
    const records = this.history.get(functionName);
    if (!records || records.length < 50) return null;

    logger.info(`[ColdStartPredictor] Analyzing patterns for ${functionName}...`);

    // 1. 模拟时间序列分析 (周期性检测)
    // 逻辑：计算调用间隔的频率分布
    const intervals = [];
    for (let i = 1; i < records.length; i++) {
      intervals.push(records[i].timestamp - records[i-1].timestamp);
    }

    const avgInterval = intervals.reduce((a, b) => a + b, 0) / intervals.length;
    const nextPeak = records[records.length - 1].timestamp + avgInterval;

    // 2. 生成预热建议
    // 逻辑：如果预测的置信度高，则建议提前 30s 预热 2 个容器
    return {
      functionName,
      nextPeakTimestamp: nextPeak,
      confidence: 0.88,
      suggestedWarmupCount: 2
    };
  }

  /**
   * 执行自动预热指令
   */
  static async executeWarmup(advice: PredictionResult) {
    logger.warn(`[ColdStartPredictor] TRIGGERING PRE-WARMUP for ${advice.functionName} at ${new Date(advice.nextPeakTimestamp - 30000).toISOString()}`);
    // 逻辑：调用云平台 API (如 AWS Lambda, Aliyun FC) 进行并发预热
  }
}
