// CacheOrchestrator: Private analysis, optimization, and evaluation methods

impl CacheOrchestrator {
    async fn analyze_workload_and_recommend(
        &self,
        workload: &WorkloadProfile,
    ) -> Result<StrategyRecommendation> {
        let eviction_policy = self.select_eviction_policy(workload);
        let tier_config = self.configure_tiers(workload);
        let expected_improvement = self
            .estimate_improvement(workload, &eviction_policy)
            .await?;
        let confidence = self.calculate_confidence(workload);

        Ok(StrategyRecommendation {
            eviction_policy,
            tier_config,
            expected_improvement,
            confidence,
        })
    }

    fn select_eviction_policy(&self, workload: &WorkloadProfile) -> EvictionPolicy {
        // Strategy selection logic based on workload characteristics
        if workload.temporal_locality > 0.8 {
            EvictionPolicy::LRU
        } else if workload.spatial_locality > 0.8 {
            EvictionPolicy::LFU
        } else if workload.latency_sensitivity > 0.9 {
            EvictionPolicy::TTL
        } else if workload.working_set_size > 1_000_000_000 {
            // > 1GB
            EvictionPolicy::Random // Avoid overhead for very large datasets
        } else {
            EvictionPolicy::Adaptive // Default to adaptive for balanced workloads
        }
    }

    fn configure_tiers(&self, workload: &WorkloadProfile) -> TierConfiguration {
        let mut tier_allocations = FxHashMap::default();
        let mut enabled_tiers = FxHashMap::default();
        let mut promotion_thresholds = FxHashMap::default();

        // Configure based on working set size and latency sensitivity
        if workload.latency_sensitivity > 0.8 {
            // Latency-sensitive: favor L1
            tier_allocations.insert(CacheTier::L1, workload.working_set_size / 2);
            tier_allocations.insert(CacheTier::L2, workload.working_set_size / 3);
            tier_allocations.insert(CacheTier::L3, workload.working_set_size / 6);
            promotion_thresholds.insert(CacheTier::L1, 0.3);
            promotion_thresholds.insert(CacheTier::L2, 0.6);
        } else {
            // Capacity-oriented: balanced approach
            tier_allocations.insert(CacheTier::L1, workload.working_set_size / 4);
            tier_allocations.insert(CacheTier::L2, workload.working_set_size / 2);
            tier_allocations.insert(CacheTier::L3, workload.working_set_size / 4);
            promotion_thresholds.insert(CacheTier::L1, 0.5);
            promotion_thresholds.insert(CacheTier::L2, 0.8);
        }

        // Enable all tiers by default
        enabled_tiers.insert(CacheTier::L1, true);
        enabled_tiers.insert(CacheTier::L2, true);
        enabled_tiers.insert(CacheTier::L3, true);

        TierConfiguration {
            tier_allocations,
            enabled_tiers,
            promotion_thresholds,
        }
    }

    async fn estimate_improvement(
        &self,
        _workload: &WorkloadProfile,
        _policy: &EvictionPolicy,
    ) -> Result<f64> {
        // Simplified improvement estimation
        // In practice, this would use ML models or historical data
        let current_metrics = self.get_performance_metrics();

        // Estimate based on current hit rate vs target
        let current_hit_rate = current_metrics.hit_rate;
        let target_hit_rate = 0.9;

        if current_hit_rate < target_hit_rate {
            Ok((target_hit_rate - current_hit_rate) * 0.5) // Conservative estimate
        } else {
            Ok(0.0)
        }
    }

    fn calculate_confidence(&self, workload: &WorkloadProfile) -> f64 {
        // Calculate confidence based on workload characteristics and historical data
        let evaluation_count = self.evaluation_history.read().len() as f64;
        let base_confidence = 0.5;

        // Higher confidence with more evaluation history
        let history_bonus = (evaluation_count / 100.0).min(0.3);

        // Higher confidence for well-defined workload characteristics
        let locality_bonus = (workload.temporal_locality + workload.spatial_locality) / 4.0;

        (base_confidence + history_bonus + locality_bonus).min(1.0)
    }

    async fn switch_to_recommended_strategy(
        &self,
        recommendation: StrategyRecommendation,
    ) -> Result<()> {
        // Implementation would switch to the recommended strategy
        // For now, just log the switch
        info!(
            "Switching to strategy: {:?} with expected {:.2}% improvement",
            recommendation.eviction_policy,
            recommendation.expected_improvement * 100.0
        );
        Ok(())
    }

    async fn perform_optimization_cycle(&self) -> Result<()> {
        // Collect current metrics
        let current_metrics = self.collect_current_metrics().await?;
        *self.metrics.write() = current_metrics.clone();

        // Update workload profile based on metrics
        self.update_workload_from_metrics(&current_metrics).await?;

        // Evaluate if strategy change is needed
        if self.config.auto_strategy_switching {
            self.evaluate_and_switch_if_needed().await?;
        }

        // Record evaluation
        self.record_evaluation(&current_metrics).await?;

        self.counters
            .evaluations_performed
            .fetch_add(1, Ordering::Relaxed);

        debug!("Completed optimization cycle");
        Ok(())
    }

    async fn collect_current_metrics(&self) -> Result<PerformanceMetrics> {
        // Collect metrics from active strategies
        let strategies = self.strategies.read();

        if strategies.is_empty() {
            return Ok(PerformanceMetrics::default());
        }

        // Aggregate metrics from all strategies
        let mut total_hit_rate = 0.0;
        let mut total_latency = Duration::ZERO;
        let mut total_throughput = 0.0;
        let mut count = 0;

        for strategy in strategies.values() {
            let metrics = strategy.get_stats();
            total_hit_rate += metrics.hit_rate;
            total_latency += metrics.avg_latency;
            total_throughput += metrics.throughput;
            count += 1;
        }

        if count > 0 {
            Ok(PerformanceMetrics {
                hit_rate: total_hit_rate / f64::from(count),
                avg_latency: total_latency / count as u32,
                memory_utilization: 0.0, // Would be calculated from actual usage
                throughput: total_throughput,
                effectiveness_score: total_hit_rate / f64::from(count), // Simplified
            })
        } else {
            Ok(PerformanceMetrics::default())
        }
    }

    async fn update_workload_from_metrics(&self, metrics: &PerformanceMetrics) -> Result<()> {
        let mut workload = self.workload_profile.write();

        // Update workload based on observed metrics
        workload.request_rate = metrics.throughput;

        // Adjust target hit rate based on current performance
        if metrics.hit_rate < workload.target_hit_rate {
            workload.latency_sensitivity = (workload.latency_sensitivity * 1.1).min(1.0);
        }

        Ok(())
    }

    async fn record_evaluation(&self, metrics: &PerformanceMetrics) -> Result<()> {
        let evaluation = StrategyEvaluation {
            performance: metrics.clone(),
            timestamp: Instant::now(),
        };

        let mut history = self.evaluation_history.write();
        history.push(evaluation);

        // Keep only recent evaluations
        if history.len() > self.config.evaluation_window {
            history.remove(0);
        }

        Ok(())
    }
}
