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name: optimization-load-balancer description: Dynamic task distribution, work-stealing algorithms, queue management, and adaptive load balancing. Use for optimizing task execution across agents, preventing overload, and maximizing throughput.

Load Balancing Coordinator Skill

Overview

This skill provides comprehensive load balancing capabilities including work-stealing algorithms, dynamic task distribution, queue management, and adaptive resource allocation for optimal swarm coordination.

When to Use

• Distributing tasks across multiple agents efficiently
• Preventing agent overload while maximizing utilization
• Implementing fair scheduling across different task priorities
• Optimizing throughput in distributed systems
• Handling variable workloads with adaptive balancing
• Migrating tasks from overloaded to underloaded agents

Quick Start

Copy# Initialize load balancer
npx claude-flow agent spawn load-balancer --type coordinator

# Start load balancing
npx claude-flow load-balance --swarm-id <id> --strategy adaptive

# Monitor load distribution
npx claude-flow agent-metrics --type load-balancer

# Adjust balancing parameters
npx claude-flow config-manage --action update --config '{"stealThreshold": 5, "agingBoost": 10}'

Architecture

Copy+-----------------------------------------------------------+
|                Load Balancing Coordinator                  |
+-----------------------------------------------------------+
|  Work Stealer  |  Load Balancer  |  Queue Manager         |
+----------------+-----------------+-------------------------+
        |                |                    |
        v                v                    v
+---------------+  +-----------------+  +------------------+
| Victim Select |  | Agent Capacity  |  | Priority Queues  |
| - Heaviest    |  | - Load Tracking |  | - Critical       |
| - Threshold   |  | - Performance   |  | - High/Normal    |
| - Locality    |  | - Migration     |  | - Low/Background |
+---------------+  +-----------------+  +------------------+
        |                |                    |
        v                v                    v
+-----------------------------------------------------------+
|              Resource Optimization Engine                  |
+-----------------------------------------------------------+

Core Capabilities

1. Work-Stealing Algorithm

Efficiently redistributes work from overloaded agents:

Copy// Work-stealing configuration
const workStealing = {
  stealThreshold: 5,       // Steal when queue > 5 tasks
  stealPercentage: 0.5,    // Take 50% of victim's queue
  victimSelection: 'heaviest',  // Steal from busiest agent
  localityAware: true      // Prefer nearby agents
};

// Victim selection strategies:
// - heaviest: Steal from agent with most tasks
// - random: Random selection for fairness
// - locality: Prefer agents in same topology region

2. Dynamic Load Balancing

Real-time load distribution:

3. Queue Management

Multi-level feedback queue scheduling:

4. Resource Allocation

Multi-objective optimization:

• Minimize latency
• Maximize utilization
• Balance load
• Minimize cost

Scheduling Algorithms

Earliest Deadline First (EDF)

Copy// EDF for real-time task scheduling
const edfScheduler = {
  schedule(tasks) {
    return tasks.sort((a, b) => a.deadline - b.deadline);
  },

  // Liu & Layland utilization bound
  admissionControl(newTask, existingTasks) {
    const utilization = [...existingTasks, newTask]
      .reduce((sum, t) => sum + (t.executionTime / t.period), 0);
    return utilization <= 1.0;
  }
};

Completely Fair Scheduler (CFS)

Copy// CFS for fair task distribution
const cfsScheduler = {
  virtualRuntime: new Map(),
  weights: new Map(),

  schedule() {
    // Select task with minimum virtual runtime
    return this.getMinVirtualRuntimeTask();
  },

  updateVirtualRuntime(task, elapsedTime) {
    const weight = this.weights.get(task.id) || 1;
    const vruntime = this.virtualRuntime.get(task.id) || 0;
    this.virtualRuntime.set(task.id, vruntime + (elapsedTime / weight));
  }
};

Weighted Fair Queuing (WFQ)

Proportional bandwidth allocation based on agent weights.

MCP Integration

Copy// MCP load balancing integration
const loadBalancingIntegration = {
  // Real-time metrics collection
  async collectMetrics() {
    const [performance, bottlenecks, tokenUsage] = await Promise.all([
      mcp.performance_report({ format: 'json' }),
      mcp.bottleneck_analyze({}),
      mcp.token_usage({})
    ]);

    return { performance, bottlenecks, tokenUsage, timestamp: Date.now() };
  },

  // Execute load balancing
  async coordinateLoadBalancing(swarmId) {
    const agents = await mcp.agent_list({ swarmId });
    const metrics = await mcp.agent_metrics({});

    const rebalancing = this.calculateRebalancing(agents, metrics);

    if (rebalancing.required) {
      await mcp.load_balance({
        swarmId,
        tasks: rebalancing.taskMigrations
      });
    }

    return rebalancing;
  }
};

Circuit Breaker Pattern

Protect against cascade failures:

Copyconst circuitBreaker = {
  state: 'CLOSED',           // CLOSED, OPEN, HALF_OPEN
  failureThreshold: 5,       // Open after 5 failures
  successThreshold: 3,       // Close after 3 successes
  timeout: 60000,            // Recovery timeout (ms)

  async execute(operation, fallback) {
    if (this.state === 'OPEN' && !this.shouldAttemptReset()) {
      return fallback ? await fallback() : null;
    }

    try {
      const result = await operation();
      this.onSuccess();
      return result;
    } catch (error) {
      this.onFailure();
      if (fallback) return fallback();
      throw error;
    }
  }
};

Key Metrics

Performance Indicators

Benchmarking

Copy// Load balancer benchmarks
const benchmarks = {
  async throughputTest(taskCount, agentCount) {
    const startTime = performance.now();
    await this.distributeAndExecute(taskCount, agentCount);
    const endTime = performance.now();

    return {
      throughput: taskCount / ((endTime - startTime) / 1000),
      averageLatency: (endTime - startTime) / taskCount
    };
  },

  async loadBalanceEfficiency(tasks, agents) {
    const distribution = await this.distributeLoad(tasks, agents);
    const idealLoad = tasks.length / agents.length;

    const variance = distribution.reduce((sum, load) =>
      sum + Math.pow(load - idealLoad, 2), 0) / agents.length;

    return {
      efficiency: 1 / (1 + variance),
      loadVariance: variance
    };
  }
};

Commands Reference

Copy# Real-time load monitoring
npx claude-flow performance-report --format detailed

# Bottleneck analysis
npx claude-flow bottleneck-analyze --component swarm-coordination

# Resource utilization tracking
npx claude-flow metrics-collect --components ["load-balancer", "task-queue"]

# Configure load balancing strategy
npx claude-flow config-manage --action update \
  --config '{"strategy": "adaptive", "threshold": 0.8}'

Integration Points

Best Practices

1. Gradual Migration: Move tasks incrementally to avoid oscillation
2. Locality Awareness: Prefer local task execution to minimize latency
3. Priority Preservation: Maintain task priorities during migration
4. Monitoring: Track load balance metrics continuously
5. Adaptive Thresholds: Adjust thresholds based on workload patterns
6. Circuit Breakers: Protect against cascade failures

Example: Adaptive Load Balancing

Copy// Adaptive load balancing strategy
const adaptiveBalancer = {
  config: {
    checkInterval: 5000,     // Check every 5 seconds
    migrationThreshold: 0.3, // Migrate if imbalance > 30%
    cooldownPeriod: 30000,   // Wait 30s between migrations
    maxMigrations: 5         // Max 5 migrations per cycle
  },

  async balance(swarm) {
    const loads = await this.getAgentLoads(swarm);
    const average = loads.reduce((a, b) => a + b) / loads.length;

    const overloaded = loads.filter(l => l > average * 1.3);
    const underloaded = loads.filter(l => l < average * 0.7);

    if (overloaded.length > 0 && underloaded.length > 0) {
      await this.migrateTasks(overloaded, underloaded);
    }
  }
};

Related Skills

• optimization-monitor - Real-time performance monitoring
• optimization-resources - Resource allocation and scaling
• optimization-topology - Network topology optimization
• optimization-benchmark - Performance validation

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Version History

• 1.0.0 (2026-01-02): Initial release - converted from load-balancer agent with work-stealing, queue management, scheduling algorithms, circuit breaker pattern, and adaptive balancing