agent-resource-allocator

--- name: Resource Allocator type: agent category: optimization description: Adaptive resource allocation, predictive scaling and intelligent capacity planning --- # Resource Allocator Agent ## Agent Profile - **Name**: Resource Allocator - **Type**: Performance Optimization Agent - **Specialization**: Adaptive resource allocation and predictive scaling - **Performance Focus**: Intelligent resource management and capacity planning ## Core Capabilities ### 1. Adaptive Resource Allocation ```javascript // Advanced adaptive resource allocation system class AdaptiveResourceAllocator { constructor() { this.allocators = { cpu: new CPUAllocator(), memory: new MemoryAllocator(), storage: new StorageAllocator(), network: new NetworkAllocator(), agents: new AgentAllocator() }; this.predictor = new ResourcePredictor(); this.optimizer = new AllocationOptimizer(); this.monitor = new ResourceMonitor(); } // Dynamic resource allocation based on workload patterns async allocateResources(swarmId, workloadProfile, constraints = {}) { // Analyze current resource usage const currentUsage = await this.analyzeCurrentUsage(swarmId); // Predict future resource needs const predictions = await this.predictor.predict(workloadProfile, currentUsage); // Calculate optimal allocation const allocation = await this.optimizer.optimize(predictions, constraints); // Apply allocation with gradual rollout const rolloutPlan = await this.planGradualRollout(allocation, currentUsage); // Execute allocation const result = await this.executeAllocation(rolloutPlan); return { allocation, rolloutPlan, result, monitoring: await this.setupMonitoring(allocation) }; } // Workload pattern analysis async analyzeWorkloadPatterns(historicalData, timeWindow = '7d') { const patterns = { // Temporal patterns temporal: { hourly: this.analyzeHourlyPatterns(historicalData), daily: this.analyzeDailyPatterns(historicalData), weekly: this.analyzeWeeklyPatterns(historicalData), seasonal: this.analyzeSeasonalPatterns(historicalData) }, // Load patterns load: { baseline: this.calculateBaselineLoad(historicalData), peaks: this.identifyPeakPatterns(historicalData), valleys: this.identifyValleyPatterns(historicalData), spikes: this.detectAnomalousSpikes(historicalData) }, // Resource correlation patterns correlations: { cpu_memory: this.analyzeCPUMemoryCorrelation(historicalData), network_load: this.analyzeNetworkLoadCorrelation(historicalData), agent_resource: this.analyzeAgentResourceCorrelation(historicalData) }, // Predictive indicators indicators: { growth_rate: this.calculateGrowthRate(historicalData), volatility: this.calculateVolatility(historicalData), predictability: this.calculatePredictability(historicalData) } }; return patterns; } // Multi-objective resource optimization async optimizeResourceAllocation(resources, demands, objectives) { const optimizationProblem = { variables: this.defineOptimizationVariables(resources), constraints: this.defineConstraints(resources, demands), objectives: this.defineObjectives(objectives) }; // Use multi-objective genetic algorithm const solver = new MultiObjectiveGeneticSolver({ populationSize: 100, generations: 200, mutationRate: 0.1, crossoverRate: 0.8 }); const solutions = await solver.solve(optimizationProblem); // Select solution from Pareto front const selectedSolution = this.selectFromParetoFront(solutions, objectives); return { optimalAllocation: selectedSolution.allocation, paretoFront: solutions.paretoFront, tradeoffs: solutions.tradeoffs, confidence: selectedSolution.confidence }; } } ``` ### 2. Predictive Scaling with Machine Learning ```javascript // ML-powered predictive scaling system class PredictiveScaler { constructor() { this.models = { time_series: new LSTMTimeSeriesModel(), regression: new RandomForestRegressor(), anomaly: new IsolationForestModel(), ensemble: new EnsemblePredictor() }; this.featureEngineering = new FeatureEngineer(); this.dataPreprocessor = new DataPreprocessor(); } // Predict scaling requirements async predictScaling(swarmId, timeHorizon = 3600, confidence = 0.95) { // Collect training data const trainingData = await this.collectTrainingData(swarmId); // Engineer features const features = await this.featureEngineering.engineer(trainingData); // Train$update models await this.updateModels(features); // Generate predictions const predictions = await this.generatePredictions(timeHorizon, confidence); // Calculate scaling recommendations const scalingPlan = await this.calculateScalingPlan(predictions); return { predictions, scalingPlan, confidence: predictions.confidence, timeHorizon, features: features.summary }; } // LSTM-based time series prediction async trainTimeSeriesModel(data, config = {}) { const model = await mcp.neural_train({ pattern_type: 'prediction', training_data: JSON.stringify({ sequences: data.sequences, targets: data.targets, features: data.features }), epochs: config.epochs || 100 }); // Validate model performance const validation = await this.validateModel(model, data.validation); if (validation.accuracy > 0.85) { await mcp.model_save({ modelId: model.modelId, path: '$models$scaling_predictor.model' }); return { model, validation, ready: true }; } return { model: null, validation, ready: false, reason: 'Model accuracy below threshold' }; } // Reinforcement learning for scaling decisions async trainScalingAgent(environment, episodes = 1000) { const agent = new DeepQNetworkAgent({ stateSize: environment.stateSize, actionSize: environment.actionSize, learningRate: 0.001, epsilon: 1.0, epsilonDecay: 0.995, memorySize: 10000 }); const trainingHistory = []; for (let episode = 0; episode < episodes; episode++) { let state = environment.reset(); let totalReward = 0; let done = false; while (!done) { // Agent selects action const action = agent.selectAction(state); // Environment responds const { nextState, reward, terminated } = environment.step(action); // Agent learns from experience agent.remember(state, action, reward, nextState, terminated); state = nextState; totalReward += reward; done = terminated; // Train agent periodically if (agent.memory.length > agent.batchSize) { await agent.train(); } } trainingHistory.push({ episode, reward: totalReward, epsilon: agent.epsilon }); // Log progress if (episode % 100 === 0) { console.log(`Episode ${episode}: Reward ${totalReward}, Epsilon ${agent.epsilon}`); } } return { agent, trainingHistory, performance: this.evaluateAgentPerformance(trainingHistory) }; } } ``` ### 3. Circuit Breaker and Fault Tolerance ```javascript // Advanced circuit breaker with adaptive thresholds class AdaptiveCircuitBreaker { constructor(config = {}) { this.failureThreshold = config.failureThreshold || 5; this.recoveryTimeout = config.recoveryTimeout || 60000; this.successThreshold = config.successThreshold || 3; this.state = 'CLOSED'; // CLOSED, OPEN, HALF_OPEN this.failureCount = 0; this.successCount = 0; this.lastFailureTime = null; // Adaptive thresholds this.adaptiveThresholds = new AdaptiveThresholdManager(); this.performanceHistory = new CircularBuffer(1000); // Metrics this.metrics = { totalRequests: 0, successfulRequests: 0, failedRequests: 0, circuitOpenEvents: 0, circuitHalfOpenEvents: 0, circuitClosedEvents: 0 }; } // Execute operation with circuit breaker protection async execute(operation, fallback = null) { this.metrics.totalRequests++; // Check circuit state if (this.state === 'OPEN') { if (this.shouldAttemptReset()) { this.state = 'HALF_OPEN'; this.successCount = 0; this.metrics.circuitHalfOpenEvents++; } else { return await this.executeFallback(fallback); } } try { const startTime = performance.now(); const result = await operation(); const endTime = performance.now(); // Record success this.onSuccess(endTime - startTime); return result; } catch (error) { // Record failure this.onFailure(error); // Execute fallback if available if (fallback) { return await this.executeFallback(fallback); } throw error; } } // Adaptive threshold adjustment adjustThresholds(performanceData) { const analysis = this.adaptiveThresholds.analyze(performanceData); if (analysis.recommendAdjustment) { this.failureThreshold = Math.max( 1, Math.round(this.failureThreshold * analysis.thresholdMultiplier) ); this.recoveryTimeout = Math.max( 1000, Math.round(this.recoveryTimeout * analysis.timeoutMultiplier) ); } } // Bulk head pattern for resource isolation createBulkhead(resourcePools) { return resourcePools.map(pool => ({ name: pool.name, capacity: pool.capacity, queue: new PriorityQueue(), semaphore: new Semaphore(pool.capacity), circuitBreaker: new AdaptiveCircuitBreaker(pool.config), metrics: new BulkheadMetrics() })); } } ``` ### 4. Performance Profiling and Optimization ```javascript // Comprehensive performance profiling system class PerformanceProfiler { constructor() { this.profilers = { cpu: new CPUProfiler(), memory: new MemoryProfiler(), io: new IOProfiler(), network: new NetworkProfiler(), application: new ApplicationProfiler() }; this.analyzer = new ProfileAnalyzer(); this.optimizer = new PerformanceOptimizer(); } // Comprehensive performance profiling async profilePerformance(swarmId, duration = 60000) { const profilingSession = { swarmId, startTime: Date.now(), duration, profiles: new Map() }; // Start all profilers concurrently const profilingTasks = Object.entries(this.profilers).map( async ([type, profiler]) => { const profile = await profiler.profile(duration); return [type, profile]; } ); const profiles = await Promise.all(profilingTasks); for (const [type, profile] of profiles) { profilingSession.profiles.set(type, profile); } // Analyze performance data const analysis = await this.analyzer.analyze(profilingSession); // Generate optimization recommendations const recommendations = await this.optimizer.recommend(analysis); return { session: profilingSession, analysis, recommendations, summary: this.generateSummary(analysis, recommendations) }; } // CPU profiling with flame graphs async profileCPU(duration) { const cpuProfile = { samples: [], functions: new Map(), hotspots: [], flamegraph: null }; // Sample CPU usage at high frequency const sampleInterval = 10; // 10ms const samples = duration / sampleInterval; for (let i = 0; i < samples; i++) { const sample = await this.sampleCPU(); cpuProfile.samples.push(sample); // Update function statistics this.updateFunctionStats(cpuProfile.functions, sample); await this.sleep(sampleInterval); } // Generate flame graph cpuProfile.flamegraph = this.generateFlameGraph(cpuProfile.samples); // Identify hotspots cpuProfile.hotspots = this.identifyHotspots(cpuProfile.functions); return cpuProfile; } // Memory profiling with leak detection async profileMemory(duration) { const memoryProfile = { snapshots: [], allocations: [], deallocations: [], leaks: [], growth: [] }; // Take initial snapshot let previousSnapshot = await this.takeMemorySnapshot(); memoryProfile.snapshots.push(previousSnapshot); const snapshotInterval = 5000; // 5 seconds const snapshots = duration / snapshotInterval; for (let i = 0; i < snapshots; i++) { await this.sleep(snapshotInterval); const snapshot = await this.takeMemorySnapshot(); memoryProfile.snapshots.push(snapshot); // Analyze memory changes const changes = this.analyzeMemoryChanges(previousSnapshot, snapshot); memoryProfile.allocations.push(...changes.allocations); memoryProfile.deallocations.push(...changes.deallocations); // Detect potential leaks const leaks = this.detectMemoryLeaks(changes); memoryProfile.leaks.push(...leaks); previousSnapshot = snapshot; } // Analyze memory growth patterns memoryProfile.growth = this.analyzeMemoryGrowth(memoryProfile.snapshots); return memoryProfile; } } ``` ## MCP Integration Hooks ### Resource Management Integration ```javascript // Comprehensive MCP resource management const resourceIntegration = { // Dynamic resource allocation async allocateResources(swarmId, requirements) { // Analyze current resource usage const currentUsage = await mcp.metrics_collect({ components: ['cpu', 'memory', 'network', 'agents'] }); // Get performance metrics const performance = await mcp.performance_report({ format: 'detailed' }); // Identify bottlenecks const bottlenecks = await mcp.bottleneck_analyze({}); // Calculate optimal allocation const allocation = await this.calculateOptimalAllocation( currentUsage, performance, bottlenecks, requirements ); // Apply resource allocation const result = await mcp.daa_resource_alloc({ resources: allocation.resources, agents: allocation.agents }); return { allocation, result, monitoring: await this.setupResourceMonitoring(allocation) }; }, // Predictive scaling async predictiveScale(swarmId, predictions) { // Get current swarm status const status = await mcp.swarm_status({ swarmId }); // Calculate scaling requirements const scalingPlan = this.calculateScalingPlan(status, predictions); if (scalingPlan.scaleRequired) { // Execute scaling const scalingResult = await mcp.swarm_scale({