//! Bandwidth management and topology optimization for communication-efficient distributed training.
//!
//! This module provides comprehensive bandwidth management and network topology optimization
//! capabilities for distributed deep learning communication. It includes dynamic bandwidth
//! allocation, congestion control, topology reconfiguration, and load balancing algorithms.
//!
//! # Key Components
//!
//! - **BandwidthManager**: Core bandwidth coordination and allocation with congestion control
//! - **TopologyManager**: Network topology coordination and optimization strategies
//! - **Congestion Control**: Adaptive congestion control algorithms with RTT estimation
//! - **Topology Optimization**: Latency-aware topology reconfiguration and load balancing
//!
//! # Features
//!
//! - **Dynamic Bandwidth Allocation**: Adaptive bandwidth distribution among workers
//! - **Congestion Control**: Multiple algorithms (Reno, Cubic, BBR, Vegas, Adaptive)
//! - **Topology Optimization**: Minimize latency, maximize bandwidth, balance load
//! - **Fault-Aware Rerouting**: Automatic rerouting around failed workers
//! - **Load Balancing**: Intelligent distribution of communication load
//! - **RTT Estimation**: Smoothed round-trip time estimation for network optimization
//!
//! # Examples
//!
//! ## Basic Bandwidth Management
//! ```rust,ignore
//! use torsh_autograd::communication_efficient::management::*;
//!
//! let mut bandwidth_manager = BandwidthManager::new(1_000_000); // 1 Mbps total
//!
//! // Allocate bandwidth to workers
//! bandwidth_manager.allocate_bandwidth(1, 250_000).unwrap(); // Worker 1: 250 kbps
//! bandwidth_manager.allocate_bandwidth(2, 300_000).unwrap(); // Worker 2: 300 kbps
//!
//! // Check allocation
//! assert_eq!(bandwidth_manager.get_allocated_bandwidth(1), Some(250_000));
//! assert_eq!(bandwidth_manager.get_available_bandwidth(), 450_000);
//! ```
//!
//! ## Topology Management
//! ```rust,ignore
//! use torsh_autograd::communication_efficient::management::*;
//! use torsh_autograd::communication_efficient::config::CommunicationTopology;
//!
//! let mut topology_manager = TopologyManager::new(CommunicationTopology::Ring);
//!
//! // Optimize for latency
//! topology_manager.optimize_for_latency().unwrap();
//!
//! // Handle worker failure
//! topology_manager.reroute_around_failed_worker(3).unwrap();
//! ```
//!
//! ## Advanced Congestion Control
//! ```rust,ignore
//! use torsh_autograd::communication_efficient::management::*;
//! use std::time::Duration;
//!
//! let mut bandwidth_manager = BandwidthManager::new(1_000_000);
//!
//! // Update RTT measurement
//! bandwidth_manager.update_rtt_measurement(Duration::from_millis(120)).unwrap();
//!
//! // Adjust for congestion
//! bandwidth_manager.adjust_for_congestion(CongestionLevel::High).unwrap();
//! ```

use crate::communication_efficient::{config::CommunicationTopology, CommunicationError};
use std::collections::{HashMap, VecDeque};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// Core bandwidth management and coordination system for distributed communication.
///
/// The BandwidthManager provides dynamic bandwidth allocation, congestion control,
/// and adaptive resource management for efficient distributed training communication.
/// It implements multiple congestion control algorithms and maintains bandwidth
/// allocation fairness across workers.
#[derive(Debug)]
pub struct BandwidthManager {
    /// Total available bandwidth for allocation
    available_bandwidth: u64,
    /// Current bandwidth allocations per worker
    allocated_bandwidth: HashMap<u32, u64>,
    /// Historical bandwidth measurements for trend analysis
    bandwidth_history: VecDeque<BandwidthMeasurement>,
    /// Congestion control state and algorithms
    congestion_control: CongestionControl,
    /// Enable adaptive bandwidth allocation based on demand
    adaptive_allocation: bool,
    /// Enable fair sharing of bandwidth among workers
    fair_sharing: bool,
}

/// Bandwidth measurement data point for historical analysis and trend detection.
///
/// Contains timestamp, measured bandwidth, utilization metrics, and congestion
/// level assessment for bandwidth management decisions.
#[derive(Debug, Clone)]
pub struct BandwidthMeasurement {
    /// Timestamp when the measurement was taken
    pub timestamp: Instant,
    /// Measured bandwidth in bytes per second
    pub measured_bandwidth: u64,
    /// Bandwidth utilization ratio (0.0 to 1.0)
    pub utilization: f64,
    /// Assessed congestion level at measurement time
    pub congestion_level: CongestionLevel,
}

/// Network congestion level classification for adaptive bandwidth management.
///
/// Used to categorize current network conditions and trigger appropriate
/// congestion control responses.
#[derive(Debug, Clone, PartialEq)]
pub enum CongestionLevel {
    /// Low congestion - optimal network conditions
    Low,
    /// Moderate congestion - some bandwidth pressure
    Moderate,
    /// High congestion - significant bandwidth constraints
    High,
    /// Severe congestion - critical bandwidth limitations
    Severe,
}

/// Congestion control state and algorithm configuration for bandwidth management.
///
/// Implements multiple congestion control algorithms with adaptive window sizing,
/// RTT estimation, and state management for optimal network utilization.
#[derive(Debug)]
pub struct CongestionControl {
    /// Active congestion control algorithm
    pub algorithm: CongestionControlAlgorithm,
    /// Current congestion window size
    pub window_size: u32,
    /// Slow start threshold for congestion avoidance
    pub slow_start_threshold: u32,
    /// Current congestion control state
    pub congestion_state: CongestionState,
    /// Round-trip time estimator for network latency tracking
    pub rtt_estimator: RTTEstimator,
}

/// Available congestion control algorithms for bandwidth management.
///
/// Each algorithm implements different strategies for congestion detection
/// and response, optimized for different network characteristics.
#[derive(Debug, Clone, PartialEq)]
pub enum CongestionControlAlgorithm {
    /// TCP Reno algorithm - classic loss-based congestion control
    Reno,
    /// CUBIC algorithm - optimized for high-bandwidth networks
    Cubic,
    /// BBR algorithm - bottleneck bandwidth and RTT-based control
    BBR,
    /// TCP Vegas algorithm - delay-based congestion control
    Vegas,
    /// Adaptive algorithm - dynamically selects optimal strategy
    Adaptive,
}

/// Congestion control state machine for algorithm state management.
///
/// Tracks the current phase of congestion control algorithm operation
/// to enable appropriate response to network conditions.
#[derive(Debug, Clone, PartialEq)]
pub enum CongestionState {
    /// Slow start phase - exponential window growth
    SlowStart,
    /// Congestion avoidance phase - linear window growth
    CongestionAvoidance,
    /// Fast recovery phase - responding to packet loss
    FastRecovery,
    /// Loss recovery phase - recovering from significant loss
    LossRecovery,
}

/// Round-trip time estimator for network latency measurement and prediction.
///
/// Implements smoothed RTT estimation with variance tracking for accurate
/// network latency assessment and timeout calculation.
#[derive(Debug)]
pub struct RTTEstimator {
    /// Smoothed round-trip time estimate
    pub smoothed_rtt: Duration,
    /// RTT variance for confidence interval calculation
    pub rtt_variance: Duration,
    /// Smoothing factor for RTT updates (typically 0.125)
    pub alpha: f64,
    /// Variance smoothing factor (typically 0.25)
    pub beta: f64,
}

/// Network topology management and optimization system for distributed communication.
///
/// The TopologyManager coordinates network topology configuration, optimization,
/// and reconfiguration for optimal communication patterns in distributed training.
/// It supports dynamic topology adaptation and fault-aware rerouting.
#[allow(dead_code)]
#[derive(Debug)]
pub struct TopologyManager {
    /// Current communication topology configuration
    topology: CommunicationTopology,
    /// Node connection mapping for topology management
    node_connections: HashMap<u32, Vec<u32>>,
    /// Topology optimization configuration and objectives
    topology_optimization: TopologyOptimization,
    /// Enable dynamic topology reconfiguration
    dynamic_topology: bool,
    /// Enable fault-aware topology management
    fault_awareness: bool,
}

/// Topology optimization configuration and objective management.
///
/// Defines optimization objectives, reconfiguration frequency, and various
/// optimization strategies for network topology management.
#[derive(Debug)]
pub struct TopologyOptimization {
    /// Primary optimization objective for topology decisions
    pub optimization_objective: OptimizationObjective,
    /// Frequency of topology reconfiguration checks
    pub reconfiguration_frequency: Duration,
    /// Enable load balancing optimization
    pub load_balancing: bool,
    /// Enable latency-aware optimization
    pub latency_awareness: bool,
    /// Enable bandwidth-aware optimization
    pub bandwidth_awareness: bool,
}

/// Optimization objectives for topology management and reconfiguration.
///
/// Defines the primary goals for topology optimization decisions,
/// enabling focused optimization strategies for different scenarios.
#[derive(Debug, Clone, PartialEq)]
pub enum OptimizationObjective {
    /// Minimize communication latency
    MinimizeLatency,
    /// Maximize available bandwidth utilization
    MaximizeBandwidth,
    /// Minimize energy consumption
    MinimizeEnergy,
    /// Balance communication load across workers
    BalanceLoad,
    /// Maximize communication reliability
    MaximizeReliability,
    /// Multi-objective optimization with weighted goals
    MultiObjective,
}

impl BandwidthManager {
    /// Creates a new bandwidth manager with specified total bandwidth capacity.
    ///
    /// Initializes the bandwidth manager with CUBIC congestion control algorithm,
    /// adaptive allocation enabled, and fair sharing policies.
    ///
    /// # Arguments
    ///
    /// * `total_bandwidth` - Total available bandwidth in bytes per second
    ///
    /// # Examples
    ///
    /// ```rust,ignore
    /// use torsh_autograd::communication_efficient::management::BandwidthManager;
    ///
    /// let manager = BandwidthManager::new(1_000_000); // 1 Mbps
    /// ```
    pub fn new(total_bandwidth: u64) -> Self {
        Self {
            available_bandwidth: total_bandwidth,
            allocated_bandwidth: HashMap::new(),
            bandwidth_history: VecDeque::new(),
            congestion_control: CongestionControl {
                algorithm: CongestionControlAlgorithm::Cubic,
                window_size: 64,
                slow_start_threshold: 32,
                congestion_state: CongestionState::SlowStart,
                rtt_estimator: RTTEstimator {
                    smoothed_rtt: Duration::from_millis(100),
                    rtt_variance: Duration::from_millis(50),
                    alpha: 0.125,
                    beta: 0.25,
                },
            },
            adaptive_allocation: true,
            fair_sharing: true,
        }
    }

    /// Allocates bandwidth to a specific worker with validation and fairness checks.
    ///
    /// Performs bandwidth allocation with fair sharing enforcement and availability
    /// validation. Updates internal allocation tracking and adjusts available bandwidth.
    ///
    /// # Arguments
    ///
    /// * `worker_id` - Unique identifier for the worker
    /// * `requested_bandwidth` - Amount of bandwidth to allocate in bytes per second
    ///
    /// # Returns
    ///
    /// * `Ok(())` if allocation successful
    /// * `Err(CommunicationError::BandwidthExhausted)` if insufficient bandwidth
    ///
    /// # Examples
    ///
    /// ```rust,ignore
    /// let mut manager = BandwidthManager::new(1_000_000);
    /// manager.allocate_bandwidth(1, 250_000).unwrap();
    /// ```
    pub fn allocate_bandwidth(
        &mut self,
        worker_id: u32,
        requested_bandwidth: u64,
    ) -> Result<(), CommunicationError> {
        // Check if requested bandwidth is available
        if requested_bandwidth > self.available_bandwidth {
            return Err(CommunicationError::BandwidthExhausted);
        }

        // Apply fair sharing policy if enabled
        if self.fair_sharing {
            let total_workers = self.allocated_bandwidth.len() as u64 + 1;
            let fair_share = self.available_bandwidth / total_workers;

            if requested_bandwidth > fair_share * 2 {
                // Limit to 2x fair share to prevent monopolization
                let limited_bandwidth = fair_share * 2;
                if limited_bandwidth > self.available_bandwidth {
                    return Err(CommunicationError::BandwidthExhausted);
                }
            }
        }

        // Update allocation tracking
        if let Some(current_allocation) = self.allocated_bandwidth.get(&worker_id) {
            self.available_bandwidth += current_allocation;
        }

        self.allocated_bandwidth
            .insert(worker_id, requested_bandwidth);
        self.available_bandwidth -= requested_bandwidth;

        Ok(())
    }

    /// Deallocates bandwidth from a worker and returns it to the available pool.
    ///
    /// Removes bandwidth allocation for the specified worker and updates
    /// available bandwidth pool for future allocations.
    ///
    /// # Arguments
    ///
    /// * `worker_id` - Unique identifier for the worker
    ///
    /// # Returns
    ///
    /// * `Ok(())` if deallocation successful
    /// * `Err(CommunicationError::ConfigurationError)` if worker not found
    pub fn deallocate_bandwidth(&mut self, worker_id: u32) -> Result<(), CommunicationError> {
        if let Some(allocated) = self.allocated_bandwidth.remove(&worker_id) {
            self.available_bandwidth += allocated;
            Ok(())
        } else {
            Err(CommunicationError::ConfigurationError)
        }
    }

    /// Records a bandwidth measurement for historical analysis and trend detection.
    ///
    /// Adds measurement to historical data and maintains a sliding window of
    /// recent measurements for congestion analysis and allocation decisions.
    ///
    /// # Arguments
    ///
    /// * `measurement` - Bandwidth measurement data point
    pub fn record_bandwidth_measurement(&mut self, measurement: BandwidthMeasurement) {
        self.bandwidth_history.push_back(measurement);

        // Maintain a reasonable history size (last 100 measurements)
        if self.bandwidth_history.len() > 100 {
            self.bandwidth_history.pop_front();
        }
    }

    /// Adjusts bandwidth allocation based on detected congestion level.
    ///
    /// Implements congestion response strategies by adjusting window sizes,
    /// updating congestion state, and potentially redistributing bandwidth
    /// allocations based on congestion severity.
    ///
    /// # Arguments
    ///
    /// * `congestion_level` - Current network congestion assessment
    ///
    /// # Returns
    ///
    /// * `Ok(())` if adjustment successful
    /// * `Err(CommunicationError::AdaptationFailed)` if adjustment fails
    pub fn adjust_for_congestion(
        &mut self,
        congestion_level: CongestionLevel,
    ) -> Result<(), CommunicationError> {
        match congestion_level {
            CongestionLevel::Low => {
                // Increase window size in slow start or congestion avoidance
                if self.congestion_control.congestion_state == CongestionState::SlowStart {
                    self.congestion_control.window_size *= 2;
                } else {
                    self.congestion_control.window_size += 1;
                }
            }
            CongestionLevel::Moderate => {
                // Switch to congestion avoidance if in slow start
                if self.congestion_control.congestion_state == CongestionState::SlowStart {
                    self.congestion_control.congestion_state = CongestionState::CongestionAvoidance;
                    self.congestion_control.slow_start_threshold =
                        self.congestion_control.window_size / 2;
                }
            }
            CongestionLevel::High => {
                // Reduce window size and enter fast recovery
                self.congestion_control.window_size = self.congestion_control.window_size / 2;
                self.congestion_control.congestion_state = CongestionState::FastRecovery;
                self.congestion_control.slow_start_threshold = self.congestion_control.window_size;
            }
            CongestionLevel::Severe => {
                // Aggressive reduction and enter loss recovery
                self.congestion_control.window_size = self.congestion_control.window_size / 4;
                self.congestion_control.congestion_state = CongestionState::LossRecovery;
                self.congestion_control.slow_start_threshold = self.congestion_control.window_size;
            }
        }

        Ok(())
    }

    /// Updates RTT measurement for network latency tracking and timeout calculation.
    ///
    /// Implements RFC 6298 smoothed RTT estimation algorithm with variance
    /// calculation for accurate network latency assessment.
    ///
    /// # Arguments
    ///
    /// * `measured_rtt` - Newly measured round-trip time
    ///
    /// # Returns
    ///
    /// * `Ok(())` if update successful
    /// * `Err(CommunicationError::ProtocolError)` if measurement invalid
    pub fn update_rtt_measurement(
        &mut self,
        measured_rtt: Duration,
    ) -> Result<(), CommunicationError> {
        let rtt_estimator = &mut self.congestion_control.rtt_estimator;

        // First RTT measurement
        if rtt_estimator.smoothed_rtt == Duration::from_millis(100) {
            rtt_estimator.smoothed_rtt = measured_rtt;
            rtt_estimator.rtt_variance = measured_rtt / 2;
        } else {
            // Update smoothed RTT: SRTT = (1-α) * SRTT + α * RTT
            let alpha = rtt_estimator.alpha;
            let current_rtt_ms = rtt_estimator.smoothed_rtt.as_millis() as f64;
            let measured_rtt_ms = measured_rtt.as_millis() as f64;

            let new_rtt_ms = (1.0 - alpha) * current_rtt_ms + alpha * measured_rtt_ms;
            rtt_estimator.smoothed_rtt = Duration::from_millis(new_rtt_ms as u64);

            // Update RTT variance: RTTVAR = (1-β) * RTTVAR + β * |SRTT - RTT|
            let beta = rtt_estimator.beta;
            let rtt_diff = (new_rtt_ms - measured_rtt_ms).abs();
            let current_var_ms = rtt_estimator.rtt_variance.as_millis() as f64;

            let new_var_ms = (1.0 - beta) * current_var_ms + beta * rtt_diff;
            rtt_estimator.rtt_variance = Duration::from_millis(new_var_ms as u64);
        }

        Ok(())
    }

    /// Performs dynamic bandwidth reallocation based on current demand and performance.
    ///
    /// Analyzes historical bandwidth usage patterns and reallocates bandwidth
    /// among workers to optimize overall communication efficiency.
    ///
    /// # Returns
    ///
    /// * `Ok(())` if reallocation successful
    /// * `Err(CommunicationError::AdaptationFailed)` if reallocation fails
    pub fn dynamic_reallocation(&mut self) -> Result<(), CommunicationError> {
        if !self.adaptive_allocation {
            return Ok(());
        }

        // Analyze bandwidth utilization patterns
        let total_allocated: u64 = self.allocated_bandwidth.values().sum();
        if total_allocated == 0 {
            return Ok(());
        }

        // Calculate utilization efficiency for each worker
        let mut worker_efficiencies = HashMap::new();
        for (&worker_id, &allocated) in &self.allocated_bandwidth {
            let efficiency = self.calculate_worker_efficiency(worker_id, allocated);
            worker_efficiencies.insert(worker_id, efficiency);
        }

        // Redistribute bandwidth based on efficiency scores
        let mut new_allocations = HashMap::new();
        let total_efficiency: f64 = worker_efficiencies.values().sum();

        if total_efficiency > 0.0 {
            for (&worker_id, &efficiency) in &worker_efficiencies {
                let efficiency_ratio = efficiency / total_efficiency;
                let new_allocation = (total_allocated as f64 * efficiency_ratio) as u64;
                new_allocations.insert(worker_id, new_allocation);
            }

            // Apply new allocations
            self.allocated_bandwidth = new_allocations;
        }

        Ok(())
    }

    /// Gets the currently allocated bandwidth for a specific worker.
    ///
    /// # Arguments
    ///
    /// * `worker_id` - Unique identifier for the worker
    ///
    /// # Returns
    ///
    /// * `Some(bandwidth)` if worker has allocation
    /// * `None` if worker not found
    pub fn get_allocated_bandwidth(&self, worker_id: u32) -> Option<u64> {
        self.allocated_bandwidth.get(&worker_id).copied()
    }

    /// Gets the current available bandwidth for new allocations.
    ///
    /// # Returns
    ///
    /// * Current available bandwidth in bytes per second
    pub fn get_available_bandwidth(&self) -> u64 {
        self.available_bandwidth
    }

    /// Calculates efficiency score for a worker based on bandwidth utilization.
    ///
    /// Analyzes historical performance to determine how effectively a worker
    /// utilizes its allocated bandwidth for optimization decisions.
    ///
    /// # Arguments
    ///
    /// * `_worker_id` - Worker identifier (for future implementation)
    /// * `_allocated_bandwidth` - Currently allocated bandwidth
    ///
    /// # Returns
    ///
    /// * Efficiency score between 0.0 and 1.0
    fn calculate_worker_efficiency(&self, _worker_id: u32, _allocated_bandwidth: u64) -> f64 {
        // Placeholder implementation - would analyze historical utilization
        // and communication patterns to determine efficiency
        0.8 // Default efficiency score
    }
}

impl TopologyManager {
    /// Creates a new topology manager with specified communication topology.
    ///
    /// Initializes the topology manager with latency-focused optimization,
    /// dynamic topology reconfiguration, and fault awareness enabled.
    ///
    /// # Arguments
    ///
    /// * `topology` - Initial communication topology configuration
    ///
    /// # Examples
    ///
    /// ```rust,ignore
    /// use torsh_autograd::communication_efficient::management::TopologyManager;
    /// use torsh_autograd::communication_efficient::config::CommunicationTopology;
    ///
    /// let manager = TopologyManager::new(CommunicationTopology::Ring);
    /// ```
    pub fn new(topology: CommunicationTopology) -> Self {
        Self {
            topology,
            node_connections: HashMap::new(),
            topology_optimization: TopologyOptimization {
                optimization_objective: OptimizationObjective::MinimizeLatency,
                reconfiguration_frequency: Duration::from_secs(60),
                load_balancing: true,
                latency_awareness: true,
                bandwidth_awareness: true,
            },
            dynamic_topology: true,
            fault_awareness: true,
        }
    }

    /// Optimizes the topology configuration for minimum latency communication.
    ///
    /// Reconfigures the topology optimization objective and triggers
    /// reconfiguration planning for latency-optimal communication patterns.
    ///
    /// # Returns
    ///
    /// * `Ok(())` if optimization configuration successful
    /// * `Err(CommunicationError::TopologyError)` if optimization fails
    pub fn optimize_for_latency(&mut self) -> Result<(), CommunicationError> {
        self.topology_optimization.optimization_objective = OptimizationObjective::MinimizeLatency;
        self.topology_optimization.latency_awareness = true;

        // Trigger topology reconfiguration planning
        self.plan_topology_reconfiguration()?;

        Ok(())
    }

    /// Optimizes the topology configuration for maximum bandwidth utilization.
    ///
    /// Reconfigures the topology optimization for bandwidth-optimal
    /// communication patterns and load distribution.
    ///
    /// # Returns
    ///
    /// * `Ok(())` if optimization configuration successful
    /// * `Err(CommunicationError::TopologyError)` if optimization fails
    pub fn optimize_for_bandwidth(&mut self) -> Result<(), CommunicationError> {
        self.topology_optimization.optimization_objective =
            OptimizationObjective::MaximizeBandwidth;
        self.topology_optimization.bandwidth_awareness = true;

        // Trigger topology reconfiguration planning
        self.plan_topology_reconfiguration()?;

        Ok(())
    }

    /// Optimizes the topology configuration for balanced load distribution.
    ///
    /// Configures load balancing optimization to distribute communication
    /// load evenly across all available workers and connections.
    ///
    /// # Returns
    ///
    /// * `Ok(())` if load balancing configuration successful
    /// * `Err(CommunicationError::TopologyError)` if configuration fails
    pub fn optimize_for_load_balance(&mut self) -> Result<(), CommunicationError> {
        self.topology_optimization.optimization_objective = OptimizationObjective::BalanceLoad;
        self.topology_optimization.load_balancing = true;

        // Trigger load balancing reconfiguration
        self.balance_communication_load()?;

        Ok(())
    }

    /// Reroutes communication paths around a failed worker node.
    ///
    /// Implements fault-aware rerouting by removing failed worker connections
    /// and reconfiguring topology to maintain communication connectivity.
    ///
    /// # Arguments
    ///
    /// * `failed_worker` - Identifier of the failed worker node
    ///
    /// # Returns
    ///
    /// * `Ok(())` if rerouting successful
    /// * `Err(CommunicationError::TopologyError)` if rerouting fails
    pub fn reroute_around_failed_worker(
        &mut self,
        failed_worker: u32,
    ) -> Result<(), CommunicationError> {
        // Remove connections to failed worker
        if let Some(connections) = self.node_connections.get_mut(&failed_worker) {
            connections.clear();
        }

        // Remove failed worker from other nodes' connection lists
        for connections in self.node_connections.values_mut() {
            connections.retain(|&worker_id| worker_id != failed_worker);
        }

        // Reconfigure topology to maintain connectivity
        self.reconfigure_for_fault_tolerance(failed_worker)?;

        Ok(())
    }

    /// Performs dynamic topology reconfiguration based on current network conditions.
    ///
    /// Analyzes current network performance and reconfigures topology
    /// to optimize for the specified optimization objective.
    ///
    /// # Returns
    ///
    /// * `Ok(())` if reconfiguration successful
    /// * `Err(CommunicationError::TopologyError)` if reconfiguration fails
    pub fn reconfigure_topology(&mut self) -> Result<(), CommunicationError> {
        if !self.dynamic_topology {
            return Ok(());
        }

        match self.topology_optimization.optimization_objective {
            OptimizationObjective::MinimizeLatency => self.reconfigure_for_latency(),
            OptimizationObjective::MaximizeBandwidth => self.reconfigure_for_bandwidth(),
            OptimizationObjective::BalanceLoad => self.balance_communication_load(),
            OptimizationObjective::MaximizeReliability => self.reconfigure_for_reliability(),
            OptimizationObjective::MinimizeEnergy => self.reconfigure_for_energy_efficiency(),
            OptimizationObjective::MultiObjective => self.reconfigure_multi_objective(),
        }
    }

    /// Adds a connection between two worker nodes in the topology.
    ///
    /// Updates the node connection mapping to include the new connection
    /// for topology management and optimization.
    ///
    /// # Arguments
    ///
    /// * `from_worker` - Source worker node identifier
    /// * `to_worker` - Destination worker node identifier
    pub fn add_connection(&mut self, from_worker: u32, to_worker: u32) {
        self.node_connections
            .entry(from_worker)
            .or_insert_with(Vec::new)
            .push(to_worker);
    }

    /// Gets the list of connections for a specific worker node.
    ///
    /// # Arguments
    ///
    /// * `worker_id` - Worker node identifier
    ///
    /// # Returns
    ///
    /// * `Some(connections)` if worker exists
    /// * `None` if worker not found
    pub fn get_connections(&self, worker_id: u32) -> Option<&Vec<u32>> {
        self.node_connections.get(&worker_id)
    }

    /// Plans topology reconfiguration based on optimization objectives.
    ///
    /// Analyzes current topology and performance metrics to plan
    /// optimal reconfiguration strategy.
    fn plan_topology_reconfiguration(&mut self) -> Result<(), CommunicationError> {
        // Placeholder for topology reconfiguration planning logic
        // Would analyze current topology performance and plan changes
        Ok(())
    }

    /// Reconfigures topology for latency optimization.
    fn reconfigure_for_latency(&mut self) -> Result<(), CommunicationError> {
        // Implement latency-optimized topology reconfiguration
        // e.g., minimize hop count, prefer direct connections
        Ok(())
    }

    /// Reconfigures topology for bandwidth optimization.
    fn reconfigure_for_bandwidth(&mut self) -> Result<(), CommunicationError> {
        // Implement bandwidth-optimized topology reconfiguration
        // e.g., aggregate high-bandwidth paths, avoid bottlenecks
        Ok(())
    }

    /// Balances communication load across available connections.
    fn balance_communication_load(&mut self) -> Result<(), CommunicationError> {
        // Implement load balancing across topology connections
        // e.g., distribute traffic across multiple paths
        Ok(())
    }

    /// Reconfigures topology for reliability optimization.
    fn reconfigure_for_reliability(&mut self) -> Result<(), CommunicationError> {
        // Implement reliability-optimized topology reconfiguration
        // e.g., add redundant paths, increase connectivity
        Ok(())
    }

    /// Reconfigures topology for energy efficiency.
    fn reconfigure_for_energy_efficiency(&mut self) -> Result<(), CommunicationError> {
        // Implement energy-efficient topology reconfiguration
        // e.g., minimize active connections, optimize path lengths
        Ok(())
    }

    /// Reconfigures topology with multi-objective optimization.
    fn reconfigure_multi_objective(&mut self) -> Result<(), CommunicationError> {
        // Implement multi-objective optimization with weighted goals
        // e.g., balance latency, bandwidth, and reliability
        Ok(())
    }

    /// Reconfigures topology for fault tolerance after worker failure.
    fn reconfigure_for_fault_tolerance(
        &mut self,
        _failed_worker: u32,
    ) -> Result<(), CommunicationError> {
        // Implement fault-tolerant topology reconfiguration
        // e.g., establish alternative paths, maintain connectivity
        Ok(())
    }
}

/// Thread-safe bandwidth manager wrapper for concurrent access.
///
/// Provides thread-safe access to bandwidth management functionality
/// for use in multi-threaded distributed training environments.
pub type SharedBandwidthManager = Arc<Mutex<BandwidthManager>>;

/// Thread-safe topology manager wrapper for concurrent access.
///
/// Provides thread-safe access to topology management functionality
/// for use in multi-threaded distributed training environments.
pub type SharedTopologyManager = Arc<Mutex<TopologyManager>>;

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_bandwidth_manager_creation() {
        let manager = BandwidthManager::new(1_000_000);
        assert_eq!(manager.get_available_bandwidth(), 1_000_000);
    }

    #[test]
    fn test_bandwidth_allocation() {
        let mut manager = BandwidthManager::new(1_000_000);

        assert!(manager.allocate_bandwidth(1, 250_000).is_ok());
        assert_eq!(manager.get_allocated_bandwidth(1), Some(250_000));
        assert_eq!(manager.get_available_bandwidth(), 750_000);
    }

    #[test]
    fn test_bandwidth_deallocation() {
        let mut manager = BandwidthManager::new(1_000_000);

        manager.allocate_bandwidth(1, 250_000).unwrap();
        assert!(manager.deallocate_bandwidth(1).is_ok());
        assert_eq!(manager.get_allocated_bandwidth(1), None);
        assert_eq!(manager.get_available_bandwidth(), 1_000_000);
    }

    #[test]
    fn test_bandwidth_exhaustion() {
        let mut manager = BandwidthManager::new(100_000);

        assert!(manager.allocate_bandwidth(1, 150_000).is_err());
        assert_eq!(manager.get_available_bandwidth(), 100_000);
    }

    #[test]
    fn test_congestion_adjustment() {
        let mut manager = BandwidthManager::new(1_000_000);
        let initial_window = manager.congestion_control.window_size;

        assert!(manager.adjust_for_congestion(CongestionLevel::High).is_ok());
        assert!(manager.congestion_control.window_size < initial_window);
        assert_eq!(
            manager.congestion_control.congestion_state,
            CongestionState::FastRecovery
        );
    }

    #[test]
    fn test_rtt_update() {
        let mut manager = BandwidthManager::new(1_000_000);
        let initial_rtt = manager.congestion_control.rtt_estimator.smoothed_rtt;

        assert!(manager
            .update_rtt_measurement(Duration::from_millis(150))
            .is_ok());
        assert_ne!(
            manager.congestion_control.rtt_estimator.smoothed_rtt,
            initial_rtt
        );
    }

    #[test]
    fn test_topology_manager_creation() {
        let manager = TopologyManager::new(CommunicationTopology::Ring);
        assert_eq!(
            manager.topology_optimization.optimization_objective,
            OptimizationObjective::MinimizeLatency
        );
    }

    #[test]
    fn test_topology_optimization() {
        let mut manager = TopologyManager::new(CommunicationTopology::Ring);

        assert!(manager.optimize_for_latency().is_ok());
        assert_eq!(
            manager.topology_optimization.optimization_objective,
            OptimizationObjective::MinimizeLatency
        );

        assert!(manager.optimize_for_bandwidth().is_ok());
        assert_eq!(
            manager.topology_optimization.optimization_objective,
            OptimizationObjective::MaximizeBandwidth
        );
    }

    #[test]
    fn test_worker_rerouting() {
        let mut manager = TopologyManager::new(CommunicationTopology::Ring);

        manager.add_connection(1, 2);
        manager.add_connection(2, 3);
        manager.add_connection(3, 1);

        assert!(manager.reroute_around_failed_worker(2).is_ok());
        assert_eq!(manager.get_connections(2), Some(&vec![]));
    }

    #[test]
    fn test_connection_management() {
        let mut manager = TopologyManager::new(CommunicationTopology::Ring);

        manager.add_connection(1, 2);
        manager.add_connection(1, 3);

        let connections = manager.get_connections(1).unwrap();
        assert_eq!(connections.len(), 2);
        assert!(connections.contains(&2));
        assert!(connections.contains(&3));
    }

    #[test]
    fn test_bandwidth_measurement_recording() {
        let mut manager = BandwidthManager::new(1_000_000);

        let measurement = BandwidthMeasurement {
            timestamp: Instant::now(),
            measured_bandwidth: 800_000,
            utilization: 0.8,
            congestion_level: CongestionLevel::Moderate,
        };

        manager.record_bandwidth_measurement(measurement);
        assert_eq!(manager.bandwidth_history.len(), 1);
    }

    #[test]
    fn test_dynamic_reallocation() {
        let mut manager = BandwidthManager::new(1_000_000);

        manager.allocate_bandwidth(1, 400_000).unwrap();
        manager.allocate_bandwidth(2, 300_000).unwrap();

        assert!(manager.dynamic_reallocation().is_ok());
        // Allocations should be updated based on efficiency scores
    }
}
