@clduab11/gemini-flow/src/performance/gpu-cluster-coordinator.ts

Revolutionary AI agent swarm coordination platform with Google Services integration, multimedia processing, and production-ready monitoring. Features 8 Google AI services, quantum computing capabilities, and enterprise-grade security.

/**
 * GPU Cluster Coordinator - Distributed rendering and compute management
 * Handles GPU resource allocation, load balancing, and fault tolerance
 */

import { EventEmitter } from "events";

export interface GPUNode {
  id: string;
  type: "nvidia" | "amd" | "intel" | "apple";
  model: string;
  vram: number; // GB
  cores: number;
  clockSpeed: number; // MHz
  utilization: number; // 0-1
  temperature: number; // Celsius
  powerUsage: number; // Watts
  status: "online" | "offline" | "maintenance" | "error";
  capabilities: GPUCapability[];
  lastHeartbeat: number;
  location: {
    datacenter: string;
    rack: string;
    position: number;
  };
}

export interface GPUCapability {
  name: string;
  version: string;
  performance: number; // relative performance score
  powerEfficiency: number; // operations per watt
}

export interface RenderingTask {
  id: string;
  type: "video" | "image" | "ml" | "compute";
  priority: "low" | "medium" | "high" | "critical";
  requirements: {
    vram: number;
    cores: number;
    capabilities: string[];
    maxLatency: number; // ms
    estimatedDuration: number; // ms
  };
  data: {
    input: ArrayBuffer;
    parameters: Record<string, any>;
    outputFormat: string;
  };
  qos: {
    maxRetries: number;
    deadline: number;
    costBudget: number;
  };
  metadata: {
    userId: string;
    sessionId: string;
    timestamp: number;
  };
}

export interface ClusterMetrics {
  totalNodes: number;
  activeNodes: number;
  totalVRAM: number;
  availableVRAM: number;
  averageUtilization: number;
  powerConsumption: number;
  tasksCompleted: number;
  tasksQueued: number;
  averageLatency: number;
  errorRate: number;
}

export class GPUClusterCoordinator extends EventEmitter {
  private nodes: Map<string, GPUNode> = new Map();
  private taskQueue: PriorityQueue<RenderingTask> = new PriorityQueue();
  private activeTasks: Map<
    string,
    { task: RenderingTask; nodeId: string; startTime: number }
  > = new Map();
  private loadBalancer: LoadBalancer;
  private faultTolerance: FaultToleranceManager;
  private resourcePool: ResourcePool;
  private scheduler: TaskScheduler;

  constructor() {
    super();
    this.loadBalancer = new LoadBalancer();
    this.faultTolerance = new FaultToleranceManager();
    this.resourcePool = new ResourcePool();
    this.scheduler = new TaskScheduler();
    this.initializeCluster();
  }

  /**
   * Add GPU node to the cluster
   */
  async addNode(node: GPUNode): Promise<void> {
    this.nodes.set(node.id, node);
    await this.validateNode(node);
    this.loadBalancer.registerNode(node);
    this.resourcePool.addResources(node);

    this.emit("nodeAdded", {
      nodeId: node.id,
      capabilities: node.capabilities,
    });
    console.log(`GPU node ${node.id} (${node.model}) added to cluster`);
  }

  /**
   * Remove GPU node from cluster
   */
  async removeNode(nodeId: string): Promise<void> {
    const node = this.nodes.get(nodeId);
    if (!node) return;

    // Gracefully move active tasks to other nodes
    await this.migrateActiveTasks(nodeId);

    this.nodes.delete(nodeId);
    this.loadBalancer.unregisterNode(nodeId);
    this.resourcePool.removeResources(nodeId);

    this.emit("nodeRemoved", { nodeId });
    console.log(`GPU node ${nodeId} removed from cluster`);
  }

  /**
   * Submit rendering task to the cluster
   */
  async submitTask(task: RenderingTask): Promise<string> {
    // Validate task requirements
    await this.validateTask(task);

    // Add to priority queue
    this.taskQueue.enqueue(task, this.calculateTaskPriority(task));

    // Attempt immediate scheduling if resources available
    await this.scheduleNextTask();

    this.emit("taskSubmitted", { taskId: task.id, priority: task.priority });
    return task.id;
  }

  /**
   * Get optimal node for task execution
   */
  async selectOptimalNode(task: RenderingTask): Promise<string | null> {
    const suitableNodes = this.findSuitableNodes(task);
    if (suitableNodes.length === 0) {
      return null;
    }

    // Load balancing strategy selection
    const strategy = this.determineBalancingStrategy(task);
    return this.loadBalancer.selectNode(suitableNodes, strategy, task);
  }

  /**
   * Execute task on selected node
   */
  async executeTask(task: RenderingTask, nodeId: string): Promise<any> {
    const node = this.nodes.get(nodeId);
    if (!node || node.status !== "online") {
      throw new Error(`Node ${nodeId} not available for execution`);
    }

    // Reserve resources
    await this.resourcePool.reserveResources(nodeId, task.requirements);

    try {
      // Track active task
      this.activeTasks.set(task.id, {
        task,
        nodeId,
        startTime: Date.now(),
      });

      // Execute task with monitoring
      const result = await this.executeWithMonitoring(task, node);

      // Update metrics
      this.updateNodeMetrics(
        nodeId,
        task,
        Date.now() - this.activeTasks.get(task.id)!.startTime,
      );

      this.emit("taskCompleted", {
        taskId: task.id,
        nodeId,
        duration: Date.now() - this.activeTasks.get(task.id)!.startTime,
      });
      return result;
    } catch (error) {
      this.emit("taskFailed", {
        taskId: task.id,
        nodeId,
        error: error.message,
      });

      // Handle failure with fault tolerance
      return await this.faultTolerance.handleTaskFailure(task, nodeId, error);
    } finally {
      // Release resources
      await this.resourcePool.releaseResources(nodeId, task.requirements);
      this.activeTasks.delete(task.id);
    }
  }

  /**
   * Real-time cluster monitoring
   */
  getClusterMetrics(): ClusterMetrics {
    const nodes = Array.from(this.nodes.values());
    const activeNodes = nodes.filter((n) => n.status === "online");

    return {
      totalNodes: nodes.length,
      activeNodes: activeNodes.length,
      totalVRAM: nodes.reduce((sum, n) => sum + n.vram, 0),
      availableVRAM: this.resourcePool.getAvailableVRAM(),
      averageUtilization:
        activeNodes.reduce((sum, n) => sum + n.utilization, 0) /
        activeNodes.length,
      powerConsumption: nodes.reduce((sum, n) => sum + n.powerUsage, 0),
      tasksCompleted: this.scheduler.getCompletedTaskCount(),
      tasksQueued: this.taskQueue.size(),
      averageLatency: this.calculateAverageLatency(),
      errorRate: this.faultTolerance.getErrorRate(),
    };
  }

  /**
   * Auto-scaling based on workload
   */
  async autoScale(): Promise<void> {
    const metrics = this.getClusterMetrics();
    const queueLength = this.taskQueue.size();
    const utilizationThreshold = 0.8;

    if (metrics.averageUtilization > utilizationThreshold && queueLength > 10) {
      // Scale up: request additional nodes
      await this.requestAdditionalNodes();
    } else if (metrics.averageUtilization < 0.3 && queueLength === 0) {
      // Scale down: release underutilized nodes
      await this.releaseUnderutilizedNodes();
    }
  }

  /**
   * Health monitoring and fault detection
   */
  async monitorHealth(): Promise<void> {
    for (const [nodeId, node] of this.nodes.entries()) {
      // Check heartbeat
      if (Date.now() - node.lastHeartbeat > 30000) {
        // 30 seconds
        await this.handleNodeTimeout(nodeId);
      }

      // Check temperature
      if (node.temperature > 85) {
        // Critical temperature
        await this.handleThermalThrottling(nodeId);
      }

      // Check utilization patterns
      if (node.utilization > 0.95) {
        // Overloaded
        await this.redistributeTasks(nodeId);
      }
    }
  }

  /**
   * Predictive resource allocation
   */
  async predictResourceNeeds(): Promise<ResourcePrediction> {
    const historicalData = this.scheduler.getHistoricalData();
    const currentTrends = this.analyzeCurrentTrends();

    return {
      nextHour: this.predictHourlyNeeds(historicalData, currentTrends),
      nextDay: this.predictDailyNeeds(historicalData, currentTrends),
      peakTimes: this.identifyPeakTimes(historicalData),
      recommendations: this.generateScalingRecommendations(currentTrends),
    };
  }

  // Private helper methods
  private async initializeCluster(): Promise<void> {
    // Initialize cluster components
    setInterval(() => this.monitorHealth(), 10000); // 10 seconds
    setInterval(() => this.autoScale(), 60000); // 1 minute
    setInterval(() => this.scheduleNextTask(), 1000); // 1 second
  }

  private async validateNode(node: GPUNode): Promise<void> {
    // Node validation logic
    if (node.vram < 4) {
      throw new Error(`Node ${node.id} has insufficient VRAM`);
    }
  }

  private async validateTask(task: RenderingTask): Promise<void> {
    // Task validation logic
    if (task.requirements.vram > this.getMaxNodeVRAM()) {
      throw new Error(`Task ${task.id} requires more VRAM than available`);
    }
  }

  private calculateTaskPriority(task: RenderingTask): number {
    const priorityWeights = { low: 1, medium: 2, high: 3, critical: 4 };
    const urgencyWeight = Math.max(
      0,
      1 - (task.qos.deadline - Date.now()) / (24 * 60 * 60 * 1000),
    );

    return priorityWeights[task.priority] + urgencyWeight;
  }

  private findSuitableNodes(task: RenderingTask): GPUNode[] {
    return Array.from(this.nodes.values()).filter(
      (node) =>
        node.status === "online" &&
        node.vram >= task.requirements.vram &&
        node.cores >= task.requirements.cores &&
        this.hasRequiredCapabilities(node, task.requirements.capabilities),
    );
  }

  private hasRequiredCapabilities(node: GPUNode, required: string[]): boolean {
    const nodeCapabilities = node.capabilities.map((c) => c.name);
    return required.every((cap) => nodeCapabilities.includes(cap));
  }

  private determineBalancingStrategy(
    task: RenderingTask,
  ): "round-robin" | "least-loaded" | "capability-based" | "cost-optimized" {
    if (task.priority === "critical") {
      return "least-loaded";
    } else if (task.requirements.capabilities.length > 0) {
      return "capability-based";
    } else if (task.qos.costBudget < 100) {
      return "cost-optimized";
    } else {
      return "round-robin";
    }
  }

  private async scheduleNextTask(): Promise<void> {
    if (this.taskQueue.isEmpty()) return;

    const nextTask = this.taskQueue.peek();
    if (!nextTask) return;

    const optimalNode = await this.selectOptimalNode(nextTask);
    if (optimalNode) {
      const task = this.taskQueue.dequeue()!;
      this.executeTask(task, optimalNode).catch((error) => {
        console.error(`Task execution failed: ${error.message}`);
      });
    }
  }

  private async executeWithMonitoring(
    task: RenderingTask,
    node: GPUNode,
  ): Promise<any> {
    // Implementation for monitored task execution
    const startTime = Date.now();

    try {
      // Simulate task execution (replace with actual GPU computation)
      await new Promise((resolve) =>
        setTimeout(resolve, task.requirements.estimatedDuration),
      );

      return {
        success: true,
        duration: Date.now() - startTime,
        output: `Processed ${task.type} task on ${node.model}`,
      };
    } catch (error) {
      throw new Error(`Execution failed on node ${node.id}: ${error.message}`);
    }
  }

  private updateNodeMetrics(
    nodeId: string,
    task: RenderingTask,
    duration: number,
  ): void {
    const node = this.nodes.get(nodeId);
    if (!node) return;

    // Update utilization, temperature, etc.
    node.utilization = Math.min(1, node.utilization + 0.1);
    node.temperature += 2; // Simulated temperature increase
    node.lastHeartbeat = Date.now();
  }

  private async migrateActiveTasks(nodeId: string): Promise<void> {
    const tasksToMigrate = Array.from(this.activeTasks.entries())
      .filter(([_, info]) => info.nodeId === nodeId)
      .map(([taskId, info]) => info.task);

    for (const task of tasksToMigrate) {
      const alternativeNode = await this.selectOptimalNode(task);
      if (alternativeNode) {
        await this.executeTask(task, alternativeNode);
      } else {
        // Re-queue task
        this.taskQueue.enqueue(task, this.calculateTaskPriority(task));
      }
    }
  }

  private async handleNodeTimeout(nodeId: string): Promise<void> {
    const node = this.nodes.get(nodeId);
    if (node) {
      node.status = "offline";
      await this.migrateActiveTasks(nodeId);
      this.emit("nodeTimeout", { nodeId });
    }
  }

  private async handleThermalThrottling(nodeId: string): Promise<void> {
    const node = this.nodes.get(nodeId);
    if (node) {
      // Reduce task allocation to this node
      this.loadBalancer.setNodeWeight(nodeId, 0.5);
      this.emit("thermalThrottling", { nodeId, temperature: node.temperature });
    }
  }

  private async redistributeTasks(nodeId: string): Promise<void> {
    // Implement task redistribution logic
    this.loadBalancer.setNodeWeight(nodeId, 0.7);
  }

  private getMaxNodeVRAM(): number {
    return Math.max(...Array.from(this.nodes.values()).map((n) => n.vram));
  }

  private calculateAverageLatency(): number {
    // Implementation for average latency calculation
    return 150; // Default 150ms
  }

  private async requestAdditionalNodes(): Promise<void> {
    this.emit("scaleUpRequest", { reason: "high-utilization" });
  }

  private async releaseUnderutilizedNodes(): Promise<void> {
    this.emit("scaleDownRequest", { reason: "low-utilization" });
  }

  private analyzeCurrentTrends(): any {
    // Implementation for trend analysis
    return {};
  }

  private predictHourlyNeeds(
    historical: any,
    trends: any,
  ): ResourcePrediction["nextHour"] {
    return { vram: 100, cores: 50, estimatedTasks: 500 };
  }

  private predictDailyNeeds(
    historical: any,
    trends: any,
  ): ResourcePrediction["nextDay"] {
    return { vram: 2000, cores: 1000, estimatedTasks: 10000 };
  }

  private identifyPeakTimes(historical: any): number[] {
    return [9, 13, 17, 21]; // Peak hours
  }

  private generateScalingRecommendations(trends: any): string[] {
    return [
      "Add 2 high-memory nodes",
      "Consider GPU instances in different regions",
    ];
  }
}

// Supporting classes
class PriorityQueue<T> {
  private items: Array<{ item: T; priority: number }> = [];

  enqueue(item: T, priority: number): void {
    this.items.push({ item, priority });
    this.items.sort((a, b) => b.priority - a.priority);
  }

  dequeue(): T | undefined {
    return this.items.shift()?.item;
  }

  peek(): T | undefined {
    return this.items[0]?.item;
  }

  size(): number {
    return this.items.length;
  }

  isEmpty(): boolean {
    return this.items.length === 0;
  }
}

class LoadBalancer {
  private nodeWeights: Map<string, number> = new Map();

  registerNode(node: GPUNode): void {
    this.nodeWeights.set(node.id, 1.0);
  }

  unregisterNode(nodeId: string): void {
    this.nodeWeights.delete(nodeId);
  }

  setNodeWeight(nodeId: string, weight: number): void {
    this.nodeWeights.set(nodeId, weight);
  }

  selectNode(
    candidates: GPUNode[],
    strategy: string,
    task: RenderingTask,
  ): string | null {
    if (candidates.length === 0) return null;

    switch (strategy) {
      case "least-loaded":
        return candidates.reduce((best, current) =>
          current.utilization < best.utilization ? current : best,
        ).id;

      case "capability-based":
        return candidates.reduce((best, current) => {
          const bestScore = this.calculateCapabilityScore(best, task);
          const currentScore = this.calculateCapabilityScore(current, task);
          return currentScore > bestScore ? current : best;
        }).id;

      case "cost-optimized":
        return candidates.reduce((best, current) =>
          current.powerUsage < best.powerUsage ? current : best,
        ).id;

      default: // round-robin
        return candidates[Math.floor(Math.random() * candidates.length)].id;
    }
  }

  private calculateCapabilityScore(node: GPUNode, task: RenderingTask): number {
    return node.capabilities
      .filter((cap) => task.requirements.capabilities.includes(cap.name))
      .reduce((score, cap) => score + cap.performance, 0);
  }
}

class FaultToleranceManager {
  private errorCounts: Map<string, number> = new Map();
  private retryAttempts: Map<string, number> = new Map();

  async handleTaskFailure(
    task: RenderingTask,
    nodeId: string,
    error: any,
  ): Promise<any> {
    const currentRetries = this.retryAttempts.get(task.id) || 0;

    if (currentRetries < task.qos.maxRetries) {
      this.retryAttempts.set(task.id, currentRetries + 1);
      // Retry on different node
      throw new Error("Retrying task on different node");
    } else {
      // Task failed permanently
      this.recordError(nodeId);
      throw new Error(
        `Task ${task.id} failed permanently after ${currentRetries} retries`,
      );
    }
  }

  private recordError(nodeId: string): void {
    const current = this.errorCounts.get(nodeId) || 0;
    this.errorCounts.set(nodeId, current + 1);
  }

  getErrorRate(): number {
    const totalErrors = Array.from(this.errorCounts.values()).reduce(
      (sum, count) => sum + count,
      0,
    );
    const totalNodes = this.errorCounts.size;
    return totalNodes > 0 ? totalErrors / totalNodes : 0;
  }
}

class ResourcePool {
  private reservedResources: Map<string, { vram: number; cores: number }> =
    new Map();
  private totalResources: Map<string, { vram: number; cores: number }> =
    new Map();

  addResources(node: GPUNode): void {
    this.totalResources.set(node.id, { vram: node.vram, cores: node.cores });
    this.reservedResources.set(node.id, { vram: 0, cores: 0 });
  }

  removeResources(nodeId: string): void {
    this.totalResources.delete(nodeId);
    this.reservedResources.delete(nodeId);
  }

  async reserveResources(
    nodeId: string,
    requirements: { vram: number; cores: number },
  ): Promise<void> {
    const reserved = this.reservedResources.get(nodeId) || {
      vram: 0,
      cores: 0,
    };
    const total = this.totalResources.get(nodeId) || { vram: 0, cores: 0 };

    if (
      reserved.vram + requirements.vram > total.vram ||
      reserved.cores + requirements.cores > total.cores
    ) {
      throw new Error(`Insufficient resources on node ${nodeId}`);
    }

    reserved.vram += requirements.vram;
    reserved.cores += requirements.cores;
    this.reservedResources.set(nodeId, reserved);
  }

  async releaseResources(
    nodeId: string,
    requirements: { vram: number; cores: number },
  ): Promise<void> {
    const reserved = this.reservedResources.get(nodeId) || {
      vram: 0,
      cores: 0,
    };
    reserved.vram = Math.max(0, reserved.vram - requirements.vram);
    reserved.cores = Math.max(0, reserved.cores - requirements.cores);
    this.reservedResources.set(nodeId, reserved);
  }

  getAvailableVRAM(): number {
    let total = 0;
    for (const [nodeId, totalRes] of this.totalResources.entries()) {
      const reserved = this.reservedResources.get(nodeId) || {
        vram: 0,
        cores: 0,
      };
      total += totalRes.vram - reserved.vram;
    }
    return total;
  }
}

class TaskScheduler {
  private completedTasks: number = 0;
  private historicalData: any[] = [];

  getCompletedTaskCount(): number {
    return this.completedTasks;
  }

  incrementCompletedTasks(): void {
    this.completedTasks++;
  }

  getHistoricalData(): any[] {
    return this.historicalData;
  }
}

interface ResourcePrediction {
  nextHour: {
    vram: number;
    cores: number;
    estimatedTasks: number;
  };
  nextDay: {
    vram: number;
    cores: number;
    estimatedTasks: number;
  };
  peakTimes: number[];
  recommendations: string[];
}

export {
  LoadBalancer,
  FaultToleranceManager,
  ResourcePool,
  TaskScheduler,
  PriorityQueue,
  ResourcePrediction,
};