agent-resource-allocator

仓库: ruvnet/ruflo
安装量: 414
排名: #8005

安装

npx skills add https://github.com/ruvnet/ruflo --skill 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
// 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
// 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 // 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 // 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 // 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

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

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 // 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.
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