Module performance_optimization 

Performance Optimization Module

This module provides comprehensive performance optimization capabilities for the composable execution framework. It includes algorithms and strategies to optimize execution performance, resource utilization, throughput, latency, and overall system efficiency through intelligent optimization techniques.

Performance Optimization Architecture

The performance optimization system is built around multiple specialized optimizers:

PerformanceOptimizer (main coordinator)
├── ThroughputOptimizer      // Maximize task completion rate
├── LatencyOptimizer         // Minimize task execution latency
├── ResourceOptimizer        // Optimize resource utilization
├── EnergyOptimizer          // Minimize energy consumption
├── CacheOptimizer           // Optimize cache hit rates
├── MemoryOptimizer          // Optimize memory usage patterns
├── NetworkOptimizer         // Optimize network performance
├── LoadBalanceOptimizer     // Balance load distribution
├── PipelineOptimizer        // Optimize execution pipelines
└── PredictiveOptimizer      // ML-based performance prediction

Optimization Strategies

Throughput Optimization

• Parallel Processing: Maximize concurrent task execution
• Batch Processing: Group similar tasks for efficiency
• Pipeline Optimization: Minimize pipeline stalls
• Resource Scaling: Dynamic resource allocation

Latency Optimization

• Task Prioritization: High-priority task fast paths
• Preemptive Scheduling: Interrupt low-priority tasks
• Cache Warming: Proactive cache population
• Resource Pre-allocation: Avoid allocation delays

Resource Optimization

• Utilization Maximization: Keep resources busy
• Fragmentation Reduction: Minimize resource waste
• Locality Optimization: CPU/memory/network locality
• Dynamic Scaling: Scale resources with demand

Energy Optimization

• Frequency Scaling: Dynamic CPU frequency adjustment
• Idle State Management: Efficient power state transitions
• Thermal Management: Temperature-aware optimization
• Workload Consolidation: Reduce active hardware

Usage Examples

Basic Performance Optimization

use sklears_compose::performance_optimization::*;

// Create performance optimizer with default configuration
let mut optimizer = PerformanceOptimizer::new()?;
optimizer.initialize()?;

// Configure optimization goals
let goals = OptimizationGoals {
    primary_objective: OptimizationObjective::Throughput,
    secondary_objectives: vec![
        OptimizationObjective::ResourceUtilization,
        OptimizationObjective::EnergyEfficiency,
    ],
    constraints: OptimizationConstraints {
        max_latency: Some(Duration::from_millis(100)),
        min_throughput: Some(1000.0), // tasks/sec
        max_energy_consumption: Some(500.0), // watts
        ..Default::default()
    },
};

optimizer.set_goals(goals)?;

Advanced Throughput Optimization

// Configure throughput optimizer
let throughput_config = ThroughputOptimizerConfig {
    target_throughput: 2000.0, // tasks/sec
    optimization_strategy: ThroughputStrategy::MaxParallel,
    batch_size_optimization: true,
    pipeline_optimization: true,
    resource_scaling: true,
    load_balancing: true,
};

let throughput_optimizer = ThroughputOptimizer::new(throughput_config)?;
optimizer.add_optimizer(Box::new(throughput_optimizer))?;

// Start optimization loop
optimizer.start_optimization().await?;

Latency-Critical Optimization

// Configure for ultra-low latency
let latency_config = LatencyOptimizerConfig {
    target_latency: Duration::from_micros(500), // 500μs target
    optimization_strategy: LatencyStrategy::PreemptiveScheduling,
    enable_cache_warming: true,
    enable_preallocation: true,
    enable_fast_paths: true,
    jitter_reduction: true,
};

let latency_optimizer = LatencyOptimizer::new(latency_config)?;
optimizer.add_optimizer(Box::new(latency_optimizer))?;

Energy-Efficient Optimization

// Configure for energy efficiency
let energy_config = EnergyOptimizerConfig {
    target_efficiency: 0.9, // 90% efficiency
    enable_frequency_scaling: true,
    enable_idle_states: true,
    enable_thermal_management: true,
    workload_consolidation: true,
    green_computing_mode: true,
};

let energy_optimizer = EnergyOptimizer::new(energy_config)?;
optimizer.add_optimizer(Box::new(energy_optimizer))?;

Machine Learning-Based Optimization

// Configure predictive optimizer
let ml_config = PredictiveOptimizerConfig {
    model_type: MLModelType::NeuralNetwork,
    training_data_size: 10000,
    prediction_horizon: Duration::from_secs(60),
    learning_rate: 0.001,
    enable_online_learning: true,
    feature_engineering: true,
};

let predictive_optimizer = PredictiveOptimizer::new(ml_config)?;
optimizer.add_optimizer(Box::new(predictive_optimizer))?;

Structs

ActualImpact

Actual impact of optimization

CacheMetrics

Cache performance metrics

EnergyMetrics

Energy consumption metrics

EnergyOptimizer

Energy optimizer implementation

EnergyOptimizerConfig

Energy optimizer configuration

EnergyOptimizerState

Energy optimizer state

ExpectedImpact

Expected impact of optimization

LatencyOptimizer

Latency optimizer implementation

LatencyOptimizerConfig

Latency optimizer configuration

LatencyOptimizerState

Latency optimizer state

MLModel

ML model abstraction

NetworkPerformanceMetrics

Network performance metrics

ObjectiveWeights

Objective weights for multi-objective optimization

OptimizationConstraints

Optimization constraints

OptimizationGoals

Optimization goals and objectives

OptimizationOpportunity

Optimization opportunity

OptimizationRecommendation

Optimization recommendation

OptimizationResult

Optimization result

OptimizerConfig

Optimizer configuration

OptimizerMetrics

Optimizer metrics

OptimizerState

Optimizer state

PerformanceAnalysis

Performance analysis result

PerformanceBaselines

Performance baselines for comparison

PerformanceBottleneck

Performance bottleneck

PerformanceMetrics

Performance metrics collector

PerformanceOptimizer

Main performance optimizer coordinating all optimization strategies

PerformancePrediction

Performance prediction

PerformanceSnapshot

Performance snapshot

PerformanceTargets

Performance targets

PerformanceTrends

Performance trends analysis

PipelineMetrics

Pipeline performance metrics

PredictiveOptimizer

Machine learning-based predictive optimizer

PredictiveOptimizerConfig

Predictive optimizer configuration

PredictiveOptimizerState

Predictive optimizer state

QualityMetrics

Quality metrics

ResourceLimits

Resource usage limits

ResourceUtilizationMetrics

Resource utilization metrics

RiskAssessment

Risk assessment

SlaRequirement

SLA requirement

ThroughputOptimizer

Throughput optimizer implementation

ThroughputOptimizerConfig

Throughput optimizer configuration

ThroughputOptimizerState

Throughput optimizer state

TrainingDataPoint

Training data point

TrendData

Trend data

Enums

BottleneckType

Bottleneck types

LatencyStrategy

Latency optimization strategies

MLModelType

ML model types

OpportunityType

Opportunity types

OptimizationAction

Optimization actions

OptimizationDomain

Optimization domains

OptimizationObjective

Optimization objectives

OptimizationPhase

Optimization phases

PowerMode

Power management modes

RecommendationPriority

Recommendation priority

RecommendationType

Recommendation types

SlaMetricType

SLA metric types

ThermalState

Thermal states

ThroughputStrategy

Throughput optimization strategies

TrendDirection

Trend directions

Traits

SpecializedOptimizer

Specialized optimizer trait