@clduab11/gemini-flow
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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.
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text/typescript
/**
* PredictiveStreamingManager - Advanced adaptive buffering system
* Implements ML-based content prediction and multi-tier buffering strategies
*/
import { EventEmitter } from "events";
export interface StreamingMetrics {
bandwidth: number;
latency: number;
packetLoss: number;
cpuUsage: number;
memoryUsage: number;
userEngagement: number;
}
export interface BufferConfig {
initialSize: number;
maxSize: number;
minSize: number;
adaptationRate: number;
predictionWindow: number;
}
export interface ContentSegment {
id: string;
data: ArrayBuffer;
timestamp: number;
priority: number;
predictedUsage: number;
size: number;
}
export interface UserPattern {
userId: string;
viewingHistory: string[];
preferredQuality: string;
sessionDuration: number;
interactionPattern: "linear" | "random" | "selective";
deviceCapabilities: {
cpu: number;
memory: number;
network: string;
};
}
export class PredictiveStreamingManager extends EventEmitter {
private buffers: Map<string, Map<string, ContentSegment>> = new Map();
private userPatterns: Map<string, UserPattern> = new Map();
private networkMetrics: StreamingMetrics;
private config: BufferConfig;
private predictionModel: ContentPredictionModel;
private qualityController: QualityController;
constructor(config: BufferConfig) {
super();
this.config = config;
this.networkMetrics = this.initializeMetrics();
this.predictionModel = new ContentPredictionModel();
this.qualityController = new QualityController();
this.initializeBuffers();
}
/**
* Adaptive buffering based on network conditions and user patterns
*/
async optimizeBuffering(userId: string, contentId: string): Promise<void> {
const userPattern = this.getUserPattern(userId);
const networkCondition = await this.assessNetworkCondition();
const predictedContent = await this.predictionModel.predictNext(
userPattern,
contentId,
);
// Multi-tier buffering strategy
await this.implementTieredBuffering(
userId,
predictedContent,
networkCondition,
);
// Adaptive buffer size adjustment
this.adjustBufferSize(userId, networkCondition);
// Pre-load predicted content
await this.preloadPredictedContent(userId, predictedContent);
this.emit("bufferingOptimized", {
userId,
contentId,
bufferMetrics: this.getBufferMetrics(userId),
});
}
/**
* Implement multi-tier buffering with priority-based allocation
*/
private async implementTieredBuffering(
userId: string,
predictedContent: ContentSegment[],
networkCondition: NetworkCondition,
): Promise<void> {
const userBuffer = this.ensureUserBuffer(userId);
// Tier 1: Critical content (current + next 2 segments)
const criticalContent = predictedContent.slice(0, 3);
await this.bufferTier(
userBuffer,
criticalContent,
"critical",
networkCondition,
);
// Tier 2: Predicted high-probability content
const probableContent = predictedContent
.slice(3, 8)
.filter((c) => c.predictedUsage > 0.7);
await this.bufferTier(
userBuffer,
probableContent,
"probable",
networkCondition,
);
// Tier 3: Speculative content based on user patterns
const speculativeContent = predictedContent
.slice(8)
.filter((c) => c.predictedUsage > 0.4);
await this.bufferTier(
userBuffer,
speculativeContent,
"speculative",
networkCondition,
);
}
/**
* Dynamic buffer size adjustment based on network conditions
*/
private adjustBufferSize(
userId: string,
networkCondition: NetworkCondition,
): void {
const currentSize = this.getBufferSize(userId);
let targetSize = this.config.initialSize;
// Adjust based on network conditions
if (networkCondition.bandwidth < 1000000) {
// < 1Mbps
targetSize = Math.max(this.config.minSize, currentSize * 1.5);
} else if (networkCondition.bandwidth > 10000000) {
// > 10Mbps
targetSize = Math.min(this.config.maxSize, currentSize * 0.8);
}
// Adjust based on latency
if (networkCondition.latency > 200) {
targetSize *= 1.3;
}
this.setBufferSize(userId, targetSize);
}
/**
* Predictive content pre-loading based on ML analysis
*/
private async preloadPredictedContent(
userId: string,
predictedContent: ContentSegment[],
): Promise<void> {
const userPattern = this.getUserPattern(userId);
const availableBandwidth = this.getAvailableBandwidth();
for (const content of predictedContent) {
if (content.predictedUsage > 0.6 && availableBandwidth > content.size) {
await this.preloadSegment(userId, content);
this.updateBandwidthUsage(content.size);
}
}
}
/**
* Real-time quality adjustment based on conditions
*/
async adjustQuality(userId: string, contentId: string): Promise<string> {
const networkCondition = await this.assessNetworkCondition();
const userPattern = this.getUserPattern(userId);
const bufferHealth = this.getBufferHealth(userId);
return this.qualityController.determineOptimalQuality({
networkCondition,
userPattern,
bufferHealth,
targetLatency: 100, // ms
});
}
/**
* Network condition assessment
*/
private async assessNetworkCondition(): Promise<NetworkCondition> {
// Implement real-time network monitoring
const bandwidth = await this.measureBandwidth();
const latency = await this.measureLatency();
const packetLoss = await this.measurePacketLoss();
return {
bandwidth,
latency,
packetLoss,
stability: this.calculateStability(bandwidth, latency, packetLoss),
timestamp: Date.now(),
};
}
/**
* User pattern learning and analysis
*/
updateUserPattern(userId: string, interaction: UserInteraction): void {
const pattern = this.getUserPattern(userId);
// Update viewing history
pattern.viewingHistory.push(interaction.contentId);
if (pattern.viewingHistory.length > 100) {
pattern.viewingHistory = pattern.viewingHistory.slice(-100);
}
// Update interaction pattern
pattern.interactionPattern = this.analyzeInteractionPattern(
pattern.viewingHistory,
);
// Update session duration
pattern.sessionDuration = interaction.sessionDuration;
this.userPatterns.set(userId, pattern);
this.predictionModel.updateModel(pattern);
}
/**
* Buffer health monitoring
*/
getBufferHealth(userId: string): BufferHealth {
const userBuffer = this.buffers.get(userId);
if (!userBuffer) {
return { level: 0, trend: "stable", riskLevel: "high" };
}
const totalSize = Array.from(userBuffer.values()).reduce(
(sum, segment) => sum + segment.size,
0,
);
const maxSize = this.config.maxSize;
const level = totalSize / maxSize;
return {
level,
trend: this.calculateBufferTrend(userId),
riskLevel: level < 0.2 ? "high" : level < 0.5 ? "medium" : "low",
segmentCount: userBuffer.size,
totalSize,
};
}
/**
* Performance metrics collection
*/
getPerformanceMetrics(userId: string): PerformanceMetrics {
const bufferHealth = this.getBufferHealth(userId);
const userPattern = this.getUserPattern(userId);
return {
buffering: {
hitRate: this.calculateBufferHitRate(userId),
missRate: this.calculateBufferMissRate(userId),
efficiency: this.calculateBufferEfficiency(userId),
},
prediction: {
accuracy: this.predictionModel.getAccuracy(userId),
confidence: this.predictionModel.getConfidence(userId),
},
quality: {
averageQuality: this.qualityController.getAverageQuality(userId),
adaptationFrequency:
this.qualityController.getAdaptationFrequency(userId),
},
network: this.networkMetrics,
user: userPattern,
};
}
// Helper methods implementation
private initializeMetrics(): StreamingMetrics {
return {
bandwidth: 0,
latency: 0,
packetLoss: 0,
cpuUsage: 0,
memoryUsage: 0,
userEngagement: 0,
};
}
private initializeBuffers(): void {
// Initialize buffer pools
}
private ensureUserBuffer(userId: string): Map<string, ContentSegment> {
if (!this.buffers.has(userId)) {
this.buffers.set(userId, new Map());
}
return this.buffers.get(userId)!;
}
private getUserPattern(userId: string): UserPattern {
if (!this.userPatterns.has(userId)) {
this.userPatterns.set(userId, this.createDefaultUserPattern(userId));
}
return this.userPatterns.get(userId)!;
}
private createDefaultUserPattern(userId: string): UserPattern {
return {
userId,
viewingHistory: [],
preferredQuality: "auto",
sessionDuration: 0,
interactionPattern: "linear",
deviceCapabilities: {
cpu: 1,
memory: 1024,
network: "unknown",
},
};
}
private async bufferTier(
userBuffer: Map<string, ContentSegment>,
content: ContentSegment[],
tier: string,
networkCondition: NetworkCondition,
): Promise<void> {
// Implementation for tier-specific buffering
}
private getBufferSize(userId: string): number {
const userBuffer = this.buffers.get(userId);
if (!userBuffer) return 0;
return Array.from(userBuffer.values()).reduce(
(sum, segment) => sum + segment.size,
0,
);
}
private setBufferSize(userId: string, targetSize: number): void {
// Implementation for buffer size adjustment
}
private getBufferMetrics(userId: string): any {
return {
size: this.getBufferSize(userId),
segmentCount: this.buffers.get(userId)?.size || 0,
health: this.getBufferHealth(userId),
};
}
private getAvailableBandwidth(): number {
return this.networkMetrics.bandwidth * 0.8; // Reserve 20% for other operations
}
private async preloadSegment(
userId: string,
content: ContentSegment,
): Promise<void> {
// Implementation for content pre-loading
}
private updateBandwidthUsage(size: number): void {
// Update bandwidth usage tracking
}
private async measureBandwidth(): Promise<number> {
// Implementation for bandwidth measurement
return 5000000; // 5 Mbps default
}
private async measureLatency(): Promise<number> {
// Implementation for latency measurement
return 50; // 50ms default
}
private async measurePacketLoss(): Promise<number> {
// Implementation for packet loss measurement
return 0.01; // 1% default
}
private calculateStability(
bandwidth: number,
latency: number,
packetLoss: number,
): number {
// Calculate network stability score
return Math.max(
0,
1 - (packetLoss * 10 + Math.max(0, latency - 100) / 200),
);
}
private analyzeInteractionPattern(
history: string[],
): "linear" | "random" | "selective" {
// Analyze user interaction patterns
return "linear"; // Default implementation
}
private calculateBufferTrend(
userId: string,
): "increasing" | "decreasing" | "stable" {
// Calculate buffer trend
return "stable"; // Default implementation
}
private calculateBufferHitRate(userId: string): number {
// Calculate buffer hit rate
return 0.85; // Default implementation
}
private calculateBufferMissRate(userId: string): number {
// Calculate buffer miss rate
return 0.15; // Default implementation
}
private calculateBufferEfficiency(userId: string): number {
// Calculate buffer efficiency
return 0.9; // Default implementation
}
}
/**
* Content Prediction Model using ML algorithms
*/
class ContentPredictionModel {
private model: any; // ML model instance
private trainingData: UserPattern[] = [];
async predictNext(
userPattern: UserPattern,
currentContent: string,
): Promise<ContentSegment[]> {
// Implementation for content prediction
return [];
}
updateModel(userPattern: UserPattern): void {
this.trainingData.push(userPattern);
// Retrain model periodically
}
getAccuracy(userId: string): number {
return 0.85; // Default implementation
}
getConfidence(userId: string): number {
return 0.9; // Default implementation
}
}
/**
* Quality Controller for dynamic quality adjustment
*/
class QualityController {
determineOptimalQuality(params: {
networkCondition: NetworkCondition;
userPattern: UserPattern;
bufferHealth: BufferHealth;
targetLatency: number;
}): string {
const { networkCondition, bufferHealth, targetLatency } = params;
if (networkCondition.bandwidth > 10000000 && bufferHealth.level > 0.7) {
return "4K";
} else if (
networkCondition.bandwidth > 5000000 &&
bufferHealth.level > 0.5
) {
return "1080p";
} else if (networkCondition.bandwidth > 2000000) {
return "720p";
} else {
return "480p";
}
}
getAverageQuality(userId: string): string {
return "1080p"; // Default implementation
}
getAdaptationFrequency(userId: string): number {
return 0.1; // Default implementation
}
}
// Supporting interfaces
interface NetworkCondition {
bandwidth: number;
latency: number;
packetLoss: number;
stability: number;
timestamp: number;
}
interface BufferHealth {
level: number;
trend: "increasing" | "decreasing" | "stable";
riskLevel: "low" | "medium" | "high";
segmentCount?: number;
totalSize?: number;
}
interface UserInteraction {
contentId: string;
sessionDuration: number;
quality: string;
timestamp: number;
}
interface PerformanceMetrics {
buffering: {
hitRate: number;
missRate: number;
efficiency: number;
};
prediction: {
accuracy: number;
confidence: number;
};
quality: {
averageQuality: string;
adaptationFrequency: number;
};
network: StreamingMetrics;
user: UserPattern;
}
export {
ContentPredictionModel,
QualityController,
NetworkCondition,
BufferHealth,
UserInteraction,
PerformanceMetrics,
};