package ml import ( "encoding/json" "fmt" "path/filepath" "regexp" "strings" "sync" "time" "github.com/seaweedfs/seaweedfs/weed/glog" ) // OptimizationEngine provides a flexible, rule-based system for ML optimizations type OptimizationEngine struct { sync.RWMutex // Rule-based system rules map[string]*OptimizationRule templates map[string]*OptimizationTemplate strategies map[string]OptimizationStrategy // Learning system usagePatterns map[string]*UsagePattern adaptiveRules map[string]*AdaptiveRule // Plugin system plugins map[string]OptimizationPlugin enabled bool } // OptimizationRule defines a single optimization rule type OptimizationRule struct { ID string `json:"id"` Name string `json:"name"` Description string `json:"description"` Priority int `json:"priority"` Conditions []RuleCondition `json:"conditions"` Actions []RuleAction `json:"actions"` Metadata map[string]interface{} `json:"metadata"` } // RuleCondition defines when a rule should be applied type RuleCondition struct { Type string `json:"type"` // file_pattern, access_pattern, workload_type, etc. Property string `json:"property"` // file_path, extension, size, frequency, etc. Operator string `json:"operator"` // equals, contains, matches, greater_than, etc. Value interface{} `json:"value"` // The value to compare against Weight float64 `json:"weight"` // Weight for scoring (0.0 to 1.0) } // RuleAction defines what optimization to apply type RuleAction struct { Type string `json:"type"` // prefetch, cache, coordinate, etc. Target string `json:"target"` // file, workload, gpu, etc. Parameters map[string]interface{} `json:"parameters"` // Action-specific parameters } // OptimizationTemplate provides reusable optimization configurations type OptimizationTemplate struct { ID string `json:"id"` Name string `json:"name"` Description string `json:"description"` Category string `json:"category"` // training, inference, preprocessing, etc. Rules []string `json:"rules"` // Rule IDs to apply Parameters map[string]interface{} `json:"parameters"` // Default parameters } // OptimizationStrategy interface for pluggable optimization strategies type OptimizationStrategy interface { GetID() string GetName() string CanOptimize(context *OptimizationContext) bool Optimize(context *OptimizationContext) *OptimizationResult GetMetrics() map[string]interface{} } // OptimizationPlugin interface for framework-specific plugins type OptimizationPlugin interface { GetFrameworkName() string DetectFramework(filePath string, content []byte) float64 // Confidence score 0.0-1.0 GetOptimizationHints(context *OptimizationContext) []OptimizationHint GetDefaultRules() []*OptimizationRule GetDefaultTemplates() []*OptimizationTemplate } // OptimizationContext provides context for optimization decisions type OptimizationContext struct { // File context FilePath string `json:"file_path"` FileSize int64 `json:"file_size"` FileType string `json:"file_type"` MimeType string `json:"mime_type"` // Access context AccessPattern AccessPattern `json:"access_pattern"` AccessFrequency int64 `json:"access_frequency"` AccessHistory []time.Time `json:"access_history"` // Workload context WorkloadType string `json:"workload_type"` ProcessID int `json:"process_id"` Framework string `json:"framework"` ModelSize int64 `json:"model_size"` BatchSize int `json:"batch_size"` // System context AvailableMemory uint64 `json:"available_memory"` AvailableGPUs []int `json:"available_gpus"` NetworkBandwidth int64 `json:"network_bandwidth"` StorageIOPS int `json:"storage_iops"` // ML-specific context TrainingPhase string `json:"training_phase"` EpochNumber int `json:"epoch_number"` DatasetSize int64 `json:"dataset_size"` ModelAccuracy float64 `json:"model_accuracy"` // Custom context CustomProperties map[string]interface{} `json:"custom_properties"` } // OptimizationResult contains the results of optimization type OptimizationResult struct { Applied bool `json:"applied"` Confidence float64 `json:"confidence"` Optimizations []AppliedOptimization `json:"optimizations"` Recommendations []string `json:"recommendations"` Metrics map[string]interface{} `json:"metrics"` NextReview time.Time `json:"next_review"` } // AppliedOptimization represents a single optimization that was applied type AppliedOptimization struct { Type string `json:"type"` Target string `json:"target"` Parameters map[string]interface{} `json:"parameters"` Expected map[string]interface{} `json:"expected"` // Expected improvements Actual map[string]interface{} `json:"actual"` // Actual results (filled later) } // OptimizationHint provides hints for optimization type OptimizationHint struct { Type string `json:"type"` Description string `json:"description"` Priority int `json:"priority"` Parameters map[string]interface{} `json:"parameters"` } // UsagePattern tracks usage patterns for adaptive optimization type UsagePattern struct { sync.RWMutex ID string `json:"id"` Pattern string `json:"pattern"` // Pattern identifier Frequency int64 `json:"frequency"` // How often this pattern occurs SuccessRate float64 `json:"success_rate"` // Success rate of optimizations AvgImprovement float64 `json:"avg_improvement"` // Average improvement achieved LastSeen time.Time `json:"last_seen"` Characteristics map[string]float64 `json:"characteristics"` // Pattern characteristics } // AdaptiveRule represents a rule that adapts based on learning type AdaptiveRule struct { BaseRule *OptimizationRule `json:"base_rule"` Adaptations map[string]float64 `json:"adaptations"` // Parameter adaptations Performance PerformanceMetrics `json:"performance"` LastUpdate time.Time `json:"last_update"` } // PerformanceMetrics tracks rule performance type PerformanceMetrics struct { Applications int64 `json:"applications"` // Number of times applied Successes int64 `json:"successes"` // Number of successful applications AvgImprovement float64 `json:"avg_improvement"` // Average improvement AvgLatency float64 `json:"avg_latency"` // Average optimization latency ErrorRate float64 `json:"error_rate"` // Error rate } // NewOptimizationEngine creates a new optimization engine func NewOptimizationEngine(enabled bool) *OptimizationEngine { engine := &OptimizationEngine{ rules: make(map[string]*OptimizationRule), templates: make(map[string]*OptimizationTemplate), strategies: make(map[string]OptimizationStrategy), usagePatterns: make(map[string]*UsagePattern), adaptiveRules: make(map[string]*AdaptiveRule), plugins: make(map[string]OptimizationPlugin), enabled: enabled, } if enabled { // Load default rules and templates engine.loadDefaultConfiguration() // Initialize built-in strategies engine.initializeStrategies() glog.V(1).Infof("Optimization engine initialized with %d rules, %d templates", len(engine.rules), len(engine.templates)) } return engine } // loadDefaultConfiguration loads default rules and templates func (oe *OptimizationEngine) loadDefaultConfiguration() { // Load default ML optimization rules defaultRules := []*OptimizationRule{ { ID: "sequential_prefetch", Name: "Sequential Access Prefetching", Description: "Enable aggressive prefetching for sequential access patterns", Priority: 100, Conditions: []RuleCondition{ { Type: "access_pattern", Property: "pattern_type", Operator: "equals", Value: "sequential", Weight: 1.0, }, { Type: "file_context", Property: "size", Operator: "greater_than", Value: 1024 * 1024, // 1MB Weight: 0.8, }, }, Actions: []RuleAction{ { Type: "prefetch", Target: "file", Parameters: map[string]interface{}{ "chunk_size": 64 * 1024, // 64KB chunks "prefetch_size": 8, // 8 chunks ahead "queue_depth": 4, // 4 parallel prefetch }, }, }, }, { ID: "ml_model_cache", Name: "ML Model Caching", Description: "Optimize caching for ML model files", Priority: 90, Conditions: []RuleCondition{ { Type: "file_pattern", Property: "extension", Operator: "in", Value: []string{".pth", ".pt", ".ckpt", ".h5", ".pb", ".onnx"}, Weight: 1.0, }, { Type: "workload_context", Property: "workload_type", Operator: "in", Value: []string{"training", "inference"}, Weight: 0.9, }, }, Actions: []RuleAction{ { Type: "cache", Target: "file", Parameters: map[string]interface{}{ "cache_strategy": "persistent", "priority": "high", "eviction_policy": "lfu", }, }, }, }, { ID: "dataset_batch_optimize", Name: "Dataset Batch Optimization", Description: "Optimize access patterns for dataset files during batch processing", Priority: 85, Conditions: []RuleCondition{ { Type: "file_pattern", Property: "name_pattern", Operator: "matches", Value: ".*\\.(csv|parquet|tfrecord|arrow)$", Weight: 0.9, }, { Type: "access_pattern", Property: "batch_size", Operator: "greater_than", Value: 10, Weight: 0.8, }, }, Actions: []RuleAction{ { Type: "batch_prefetch", Target: "dataset", Parameters: map[string]interface{}{ "batch_aware": true, "shuffle_buffer": 1000, "parallel_calls": 4, }, }, }, }, } // Register default rules for _, rule := range defaultRules { oe.rules[rule.ID] = rule } // Load default templates defaultTemplates := []*OptimizationTemplate{ { ID: "pytorch_training", Name: "PyTorch Training Optimization", Description: "Optimized configuration for PyTorch training workloads", Category: "training", Rules: []string{"sequential_prefetch", "ml_model_cache", "dataset_batch_optimize"}, Parameters: map[string]interface{}{ "framework": "pytorch", "prefetch_factor": 2.0, "cache_ratio": 0.3, }, }, { ID: "tensorflow_inference", Name: "TensorFlow Inference Optimization", Description: "Optimized configuration for TensorFlow inference workloads", Category: "inference", Rules: []string{"ml_model_cache"}, Parameters: map[string]interface{}{ "framework": "tensorflow", "model_preload": true, "batch_inference": true, }, }, { ID: "generic_ml_training", Name: "Generic ML Training", Description: "General-purpose optimization for ML training", Category: "training", Rules: []string{"sequential_prefetch", "dataset_batch_optimize"}, Parameters: map[string]interface{}{ "adaptive": true, "learning_rate": 0.001, }, }, } // Register default templates for _, template := range defaultTemplates { oe.templates[template.ID] = template } } // initializeStrategies initializes built-in optimization strategies func (oe *OptimizationEngine) initializeStrategies() { // Register built-in strategies oe.strategies["adaptive_prefetch"] = &AdaptivePrefetchStrategy{} oe.strategies["intelligent_cache"] = &IntelligentCacheStrategy{} oe.strategies["workload_coordination"] = &WorkloadCoordinationStrategy{} } // RegisterPlugin registers an optimization plugin func (oe *OptimizationEngine) RegisterPlugin(plugin OptimizationPlugin) error { oe.Lock() defer oe.Unlock() frameworkName := plugin.GetFrameworkName() if _, exists := oe.plugins[frameworkName]; exists { return fmt.Errorf("plugin for framework '%s' already registered", frameworkName) } oe.plugins[frameworkName] = plugin // Load plugin's default rules and templates for _, rule := range plugin.GetDefaultRules() { oe.rules[rule.ID] = rule } for _, template := range plugin.GetDefaultTemplates() { oe.templates[template.ID] = template } glog.V(1).Infof("Registered optimization plugin for framework: %s", frameworkName) return nil } // OptimizeAccess applies optimization for file access func (oe *OptimizationEngine) OptimizeAccess(context *OptimizationContext) *OptimizationResult { if !oe.enabled { return &OptimizationResult{Applied: false} } oe.RLock() defer oe.RUnlock() result := &OptimizationResult{ Applied: false, Confidence: 0.0, Optimizations: make([]AppliedOptimization, 0), Recommendations: make([]string, 0), Metrics: make(map[string]interface{}), NextReview: time.Now().Add(5 * time.Minute), } // Enhance context with framework detection oe.enhanceContext(context) // Find applicable rules applicableRules := oe.findApplicableRules(context) if len(applicableRules) == 0 { glog.V(3).Infof("No applicable rules found for context: %+v", context) return result } // Sort rules by priority and confidence sortedRules := oe.sortRulesByPriority(applicableRules, context) // Apply top rules totalConfidence := 0.0 appliedCount := 0 for _, ruleMatch := range sortedRules { if appliedCount >= 5 { // Limit number of applied optimizations break } optimization := oe.applyRule(ruleMatch.Rule, context, ruleMatch.Confidence) if optimization != nil { result.Optimizations = append(result.Optimizations, *optimization) totalConfidence += ruleMatch.Confidence appliedCount++ result.Applied = true } } // Calculate overall confidence if appliedCount > 0 { result.Confidence = totalConfidence / float64(appliedCount) } // Generate recommendations result.Recommendations = oe.generateRecommendations(context, sortedRules) // Update usage patterns for learning oe.updateUsagePatterns(context, result) glog.V(2).Infof("Applied %d optimizations with confidence %.2f", appliedCount, result.Confidence) return result } // enhanceContext enhances the optimization context with additional information func (oe *OptimizationEngine) enhanceContext(context *OptimizationContext) { // Detect framework using plugins if context.Framework == "" { context.Framework = oe.detectFramework(context.FilePath, nil) } // Enhance with file type detection if context.FileType == "" { context.FileType = oe.detectFileType(context.FilePath) } // Add pattern-based enhancements if context.CustomProperties == nil { context.CustomProperties = make(map[string]interface{}) } // Add file-based hints ext := strings.ToLower(filepath.Ext(context.FilePath)) context.CustomProperties["file_extension"] = ext context.CustomProperties["is_model_file"] = oe.isModelFile(ext) context.CustomProperties["is_dataset_file"] = oe.isDatasetFile(ext) } // detectFramework detects ML framework from file path and content func (oe *OptimizationEngine) detectFramework(filePath string, content []byte) string { bestFramework := "" bestScore := 0.0 for _, plugin := range oe.plugins { score := plugin.DetectFramework(filePath, content) if score > bestScore { bestScore = score bestFramework = plugin.GetFrameworkName() } } // Fallback to simple pattern matching if bestFramework == "" { ext := strings.ToLower(filepath.Ext(filePath)) switch ext { case ".pth", ".pt": return "pytorch" case ".h5", ".hdf5": return "tensorflow" case ".ckpt": return "tensorflow" case ".pb": return "tensorflow" case ".onnx": return "onnx" } } return bestFramework } // detectFileType detects the type of file for optimization purposes func (oe *OptimizationEngine) detectFileType(filePath string) string { ext := strings.ToLower(filepath.Ext(filePath)) if oe.isModelFile(ext) { return "model" } if oe.isDatasetFile(ext) { return "dataset" } if oe.isConfigFile(ext) { return "config" } if oe.isLogFile(ext) { return "log" } return "unknown" } // Helper functions for file type detection func (oe *OptimizationEngine) isModelFile(ext string) bool { modelExtensions := []string{".pth", ".pt", ".ckpt", ".h5", ".hdf5", ".pb", ".onnx", ".pkl", ".model"} for _, modelExt := range modelExtensions { if ext == modelExt { return true } } return false } func (oe *OptimizationEngine) isDatasetFile(ext string) bool { datasetExtensions := []string{".csv", ".json", ".parquet", ".arrow", ".tfrecord", ".hdf5", ".npy", ".npz"} for _, dataExt := range datasetExtensions { if ext == dataExt { return true } } return false } func (oe *OptimizationEngine) isConfigFile(ext string) bool { configExtensions := []string{".yaml", ".yml", ".json", ".toml", ".ini", ".conf", ".cfg"} for _, confExt := range configExtensions { if ext == confExt { return true } } return false } func (oe *OptimizationEngine) isLogFile(ext string) bool { return ext == ".log" || ext == ".txt" } // RuleMatch represents a rule with its confidence score type RuleMatch struct { Rule *OptimizationRule Confidence float64 } // findApplicableRules finds rules that apply to the given context func (oe *OptimizationEngine) findApplicableRules(context *OptimizationContext) []*RuleMatch { matches := make([]*RuleMatch, 0) for _, rule := range oe.rules { confidence := oe.evaluateRuleConditions(rule, context) if confidence > 0.5 { // Minimum confidence threshold matches = append(matches, &RuleMatch{ Rule: rule, Confidence: confidence, }) } } return matches } // evaluateRuleConditions evaluates rule conditions against context func (oe *OptimizationEngine) evaluateRuleConditions(rule *OptimizationRule, context *OptimizationContext) float64 { if len(rule.Conditions) == 0 { return 0.0 } totalWeight := 0.0 matchedWeight := 0.0 for _, condition := range rule.Conditions { totalWeight += condition.Weight if oe.evaluateCondition(condition, context) { matchedWeight += condition.Weight } } if totalWeight == 0 { return 0.0 } return matchedWeight / totalWeight } // evaluateCondition evaluates a single condition func (oe *OptimizationEngine) evaluateCondition(condition RuleCondition, context *OptimizationContext) bool { var contextValue interface{} // Extract context value based on condition type and property switch condition.Type { case "file_pattern": contextValue = oe.getFileProperty(condition.Property, context) case "access_pattern": contextValue = oe.getAccessProperty(condition.Property, context) case "workload_context": contextValue = oe.getWorkloadProperty(condition.Property, context) case "system_context": contextValue = oe.getSystemProperty(condition.Property, context) default: return false } // Evaluate based on operator return oe.evaluateOperator(condition.Operator, contextValue, condition.Value) } // Property extraction methods func (oe *OptimizationEngine) getFileProperty(property string, context *OptimizationContext) interface{} { switch property { case "path": return context.FilePath case "extension": return strings.ToLower(filepath.Ext(context.FilePath)) case "size": return context.FileSize case "type": return context.FileType case "name_pattern": return filepath.Base(context.FilePath) default: return nil } } func (oe *OptimizationEngine) getAccessProperty(property string, context *OptimizationContext) interface{} { switch property { case "pattern_type": return context.AccessPattern.String() case "frequency": return context.AccessFrequency case "batch_size": return context.BatchSize default: return nil } } func (oe *OptimizationEngine) getWorkloadProperty(property string, context *OptimizationContext) interface{} { switch property { case "workload_type": return context.WorkloadType case "framework": return context.Framework case "training_phase": return context.TrainingPhase default: return nil } } func (oe *OptimizationEngine) getSystemProperty(property string, context *OptimizationContext) interface{} { switch property { case "available_memory": return context.AvailableMemory case "gpu_count": return len(context.AvailableGPUs) default: return nil } } // evaluateOperator evaluates comparison operators func (oe *OptimizationEngine) evaluateOperator(operator string, contextValue, ruleValue interface{}) bool { switch operator { case "equals": return contextValue == ruleValue case "contains": if contextStr, ok := contextValue.(string); ok { if ruleStr, ok := ruleValue.(string); ok { return strings.Contains(contextStr, ruleStr) } } case "matches": if contextStr, ok := contextValue.(string); ok { if ruleStr, ok := ruleValue.(string); ok { matched, _ := regexp.MatchString(ruleStr, contextStr) return matched } } case "in": if ruleSlice, ok := ruleValue.([]interface{}); ok { for _, item := range ruleSlice { if contextValue == item { return true } } } if ruleSlice, ok := ruleValue.([]string); ok { if contextStr, ok := contextValue.(string); ok { for _, item := range ruleSlice { if contextStr == item { return true } } } } case "greater_than": return oe.compareNumbers(contextValue, ruleValue, ">") case "less_than": return oe.compareNumbers(contextValue, ruleValue, "<") case "greater_equal": return oe.compareNumbers(contextValue, ruleValue, ">=") case "less_equal": return oe.compareNumbers(contextValue, ruleValue, "<=") } return false } // compareNumbers compares numeric values func (oe *OptimizationEngine) compareNumbers(a, b interface{}, op string) bool { aFloat, aOk := oe.toFloat64(a) bFloat, bOk := oe.toFloat64(b) if !aOk || !bOk { return false } switch op { case ">": return aFloat > bFloat case "<": return aFloat < bFloat case ">=": return aFloat >= bFloat case "<=": return aFloat <= bFloat default: return false } } // toFloat64 converts various numeric types to float64 func (oe *OptimizationEngine) toFloat64(value interface{}) (float64, bool) { switch v := value.(type) { case int: return float64(v), true case int32: return float64(v), true case int64: return float64(v), true case uint: return float64(v), true case uint32: return float64(v), true case uint64: return float64(v), true case float32: return float64(v), true case float64: return v, true default: return 0, false } } // sortRulesByPriority sorts rules by priority and confidence func (oe *OptimizationEngine) sortRulesByPriority(matches []*RuleMatch, context *OptimizationContext) []*RuleMatch { // Simple sorting by combined score (priority * confidence) for i := 0; i < len(matches)-1; i++ { for j := i + 1; j < len(matches); j++ { scoreI := float64(matches[i].Rule.Priority) * matches[i].Confidence scoreJ := float64(matches[j].Rule.Priority) * matches[j].Confidence if scoreI < scoreJ { matches[i], matches[j] = matches[j], matches[i] } } } return matches } // applyRule applies a single optimization rule func (oe *OptimizationEngine) applyRule(rule *OptimizationRule, context *OptimizationContext, confidence float64) *AppliedOptimization { if len(rule.Actions) == 0 { return nil } // For now, apply the first action (could be extended to handle multiple actions) action := rule.Actions[0] optimization := &AppliedOptimization{ Type: action.Type, Target: action.Target, Parameters: make(map[string]interface{}), Expected: make(map[string]interface{}), Actual: make(map[string]interface{}), } // Copy parameters for k, v := range action.Parameters { optimization.Parameters[k] = v } // Set expected improvements based on rule type optimization.Expected["confidence"] = confidence optimization.Expected["rule_id"] = rule.ID optimization.Expected["improvement_estimate"] = confidence * 0.5 // Simple estimation glog.V(3).Infof("Applied rule '%s' with confidence %.2f", rule.ID, confidence) return optimization } // generateRecommendations generates optimization recommendations func (oe *OptimizationEngine) generateRecommendations(context *OptimizationContext, matches []*RuleMatch) []string { recommendations := make([]string, 0) // Add general recommendations based on context if context.Framework != "" { recommendations = append(recommendations, fmt.Sprintf("Consider using %s-specific optimizations", context.Framework)) } if context.FileType == "model" && context.FileSize > 100*1024*1024 { recommendations = append(recommendations, "Large model file detected - consider model compression or sharding") } if context.AccessPattern == SequentialAccess { recommendations = append(recommendations, "Sequential access pattern detected - increase prefetch buffer size") } return recommendations } // updateUsagePatterns updates usage patterns for adaptive learning func (oe *OptimizationEngine) updateUsagePatterns(context *OptimizationContext, result *OptimizationResult) { patternKey := oe.generatePatternKey(context) oe.Lock() defer oe.Unlock() pattern, exists := oe.usagePatterns[patternKey] if !exists { pattern = &UsagePattern{ ID: patternKey, Pattern: patternKey, Frequency: 0, SuccessRate: 0.0, AvgImprovement: 0.0, LastSeen: time.Now(), Characteristics: make(map[string]float64), } oe.usagePatterns[patternKey] = pattern } pattern.Lock() pattern.Frequency++ pattern.LastSeen = time.Now() // Update characteristics pattern.Characteristics["file_size"] = float64(context.FileSize) pattern.Characteristics["access_frequency"] = float64(context.AccessFrequency) pattern.Characteristics["confidence"] = result.Confidence pattern.Unlock() } // generatePatternKey generates a key for pattern identification func (oe *OptimizationEngine) generatePatternKey(context *OptimizationContext) string { key := fmt.Sprintf("fw:%s|type:%s|pattern:%s|phase:%s", context.Framework, context.FileType, context.AccessPattern.String(), context.TrainingPhase) return key } // GetMetrics returns optimization engine metrics func (oe *OptimizationEngine) GetMetrics() map[string]interface{} { oe.RLock() defer oe.RUnlock() metrics := map[string]interface{}{ "enabled": oe.enabled, "rules_count": len(oe.rules), "templates_count": len(oe.templates), "strategies_count": len(oe.strategies), "plugins_count": len(oe.plugins), "patterns_learned": len(oe.usagePatterns), } // Add pattern statistics totalFrequency := int64(0) for _, pattern := range oe.usagePatterns { pattern.RLock() totalFrequency += pattern.Frequency pattern.RUnlock() } metrics["total_pattern_frequency"] = totalFrequency return metrics } // LoadConfiguration loads optimization rules and templates from configuration func (oe *OptimizationEngine) LoadConfiguration(configData []byte) error { var config struct { Rules []*OptimizationRule `json:"rules"` Templates []*OptimizationTemplate `json:"templates"` } if err := json.Unmarshal(configData, &config); err != nil { return fmt.Errorf("failed to parse configuration: %w", err) } oe.Lock() defer oe.Unlock() // Load rules for _, rule := range config.Rules { oe.rules[rule.ID] = rule glog.V(2).Infof("Loaded optimization rule: %s", rule.ID) } // Load templates for _, template := range config.Templates { oe.templates[template.ID] = template glog.V(2).Infof("Loaded optimization template: %s", template.ID) } return nil } // Shutdown gracefully shuts down the optimization engine func (oe *OptimizationEngine) Shutdown() { oe.Lock() defer oe.Unlock() oe.enabled = false glog.V(1).Infof("Optimization engine shutdown complete") } // Built-in optimization strategies // AdaptivePrefetchStrategy implements adaptive prefetching type AdaptivePrefetchStrategy struct{} func (s *AdaptivePrefetchStrategy) GetID() string { return "adaptive_prefetch" } func (s *AdaptivePrefetchStrategy) GetName() string { return "Adaptive Prefetch Strategy" } func (s *AdaptivePrefetchStrategy) CanOptimize(context *OptimizationContext) bool { return context.AccessPattern == SequentialAccess || context.AccessPattern == StridedAccess } func (s *AdaptivePrefetchStrategy) Optimize(context *OptimizationContext) *OptimizationResult { return &OptimizationResult{ Applied: true, Confidence: 0.8, Optimizations: []AppliedOptimization{ { Type: "prefetch", Target: "file", Parameters: map[string]interface{}{ "strategy": "adaptive", "chunk_size": 64 * 1024, }, }, }, } } func (s *AdaptivePrefetchStrategy) GetMetrics() map[string]interface{} { return map[string]interface{}{ "strategy": "adaptive_prefetch", "applications": 0, } } // IntelligentCacheStrategy implements intelligent caching type IntelligentCacheStrategy struct{} func (s *IntelligentCacheStrategy) GetID() string { return "intelligent_cache" } func (s *IntelligentCacheStrategy) GetName() string { return "Intelligent Cache Strategy" } func (s *IntelligentCacheStrategy) CanOptimize(context *OptimizationContext) bool { return context.FileType == "model" || context.AccessFrequency > 10 } func (s *IntelligentCacheStrategy) Optimize(context *OptimizationContext) *OptimizationResult { return &OptimizationResult{ Applied: true, Confidence: 0.7, Optimizations: []AppliedOptimization{ { Type: "cache", Target: "file", Parameters: map[string]interface{}{ "strategy": "intelligent", "priority": "high", }, }, }, } } func (s *IntelligentCacheStrategy) GetMetrics() map[string]interface{} { return map[string]interface{}{ "strategy": "intelligent_cache", "applications": 0, } } // WorkloadCoordinationStrategy implements workload coordination type WorkloadCoordinationStrategy struct{} func (s *WorkloadCoordinationStrategy) GetID() string { return "workload_coordination" } func (s *WorkloadCoordinationStrategy) GetName() string { return "Workload Coordination Strategy" } func (s *WorkloadCoordinationStrategy) CanOptimize(context *OptimizationContext) bool { return context.WorkloadType != "" && context.ProcessID > 0 } func (s *WorkloadCoordinationStrategy) Optimize(context *OptimizationContext) *OptimizationResult { return &OptimizationResult{ Applied: true, Confidence: 0.6, Optimizations: []AppliedOptimization{ { Type: "coordinate", Target: "workload", Parameters: map[string]interface{}{ "strategy": "coordination", "priority": "normal", }, }, }, } } func (s *WorkloadCoordinationStrategy) GetMetrics() map[string]interface{} { return map[string]interface{}{ "strategy": "workload_coordination", "applications": 0, } }