resource-allocator.md•19.4 kB
---
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({
swarmId,
targetSize: scalingPlan.targetSize
});
// Optimize topology after scaling
if (scalingResult.success) {
await mcp.topology_optimize({ swarmId });
}
return {
scaled: true,
plan: scalingPlan,
result: scalingResult
};
}
return {
scaled: false,
reason: 'No scaling required',
plan: scalingPlan
};
},
// Performance optimization
async optimizePerformance(swarmId) {
// Collect comprehensive metrics
const metrics = await Promise.all([
mcp.performance_report({ format: 'json' }),
mcp.bottleneck_analyze({}),
mcp.agent_metrics({}),
mcp.metrics_collect({ components: ['system', 'agents', 'coordination'] })
]);
const [performance, bottlenecks, agentMetrics, systemMetrics] = metrics;
// Generate optimization recommendations
const optimizations = await this.generateOptimizations({
performance,
bottlenecks,
agentMetrics,
systemMetrics
});
// Apply optimizations
const results = await this.applyOptimizations(swarmId, optimizations);
return {
optimizations,
results,
impact: await this.measureOptimizationImpact(swarmId, results)
};
}
};
```
## Operational Commands
### Resource Management Commands
```bash
# Analyze resource usage
npx claude-flow metrics-collect --components ["cpu", "memory", "network"]
# Optimize resource allocation
npx claude-flow daa-resource-alloc --resources <resource-config>
# Predictive scaling
npx claude-flow swarm-scale --swarm-id <id> --target-size <size>
# Performance profiling
npx claude-flow performance-report --format detailed --timeframe 24h
# Circuit breaker configuration
npx claude-flow fault-tolerance --strategy circuit-breaker --config <config>
```
### Optimization Commands
```bash
# Run performance optimization
npx claude-flow optimize-performance --swarm-id <id> --strategy adaptive
# Generate resource forecasts
npx claude-flow forecast-resources --time-horizon 3600 --confidence 0.95
# Profile system performance
npx claude-flow profile-performance --duration 60000 --components all
# Analyze bottlenecks
npx claude-flow bottleneck-analyze --component swarm-coordination
```
## Integration Points
### With Other Optimization Agents
- **Load Balancer**: Provides resource allocation data for load balancing decisions
- **Performance Monitor**: Shares performance metrics and bottleneck analysis
- **Topology Optimizer**: Coordinates resource allocation with topology changes
### With Swarm Infrastructure
- **Task Orchestrator**: Allocates resources for task execution
- **Agent Coordinator**: Manages agent resource requirements
- **Memory System**: Stores resource allocation history and patterns
## Performance Metrics
### Resource Allocation KPIs
```javascript
// Resource allocation performance metrics
const allocationMetrics = {
efficiency: {
utilization_rate: this.calculateUtilizationRate(),
waste_percentage: this.calculateWastePercentage(),
allocation_accuracy: this.calculateAllocationAccuracy(),
prediction_accuracy: this.calculatePredictionAccuracy()
},
performance: {
allocation_latency: this.calculateAllocationLatency(),
scaling_response_time: this.calculateScalingResponseTime(),
optimization_impact: this.calculateOptimizationImpact(),
cost_efficiency: this.calculateCostEfficiency()
},
reliability: {
availability: this.calculateAvailability(),
fault_tolerance: this.calculateFaultTolerance(),
recovery_time: this.calculateRecoveryTime(),
circuit_breaker_effectiveness: this.calculateCircuitBreakerEffectiveness()
}
};
```
This Resource Allocator agent provides comprehensive adaptive resource allocation with ML-powered predictive scaling, fault tolerance patterns, and advanced performance optimization for efficient swarm resource management.