Agent Load Balancer

// Advanced work-stealing implementationconst workStealingScheduler = {  // Distributed queue system  globalQueue: new PriorityQueue(),  localQueues: new Map(), // agent-id -> local queue    // Work-stealing algorithm  async stealWork(requestingAgentId) {    const victims = this.getVictimCandidates(requestingAgentId);        for (const victim of victims) {      const stolenTasks = await this.attemptSteal(victim, requestingAgentId);      if (stolenTasks.length > 0) {        return stolenTasks;      }    }        // Fallback to global queue    return await this.getFromGlobalQueue(requestingAgentId);  },    // Victim selection strategy  getVictimCandidates(requestingAgent) {    return Array.from(this.localQueues.entries())      .filter(([agentId, queue]) =>         agentId !== requestingAgent &&         queue.size() > this.stealThreshold      )      .sort((a, b) => b[1].size() - a[1].size()) // Heaviest first      .map(([agentId]) => agentId);  }};

// Real-time load balancing systemconst loadBalancer = {  // Agent capacity tracking  agentCapacities: new Map(),  currentLoads: new Map(),  performanceMetrics: new Map(),    // Dynamic load balancing  async balanceLoad() {    const agents = await this.getActiveAgents();    const loadDistribution = this.calculateLoadDistribution(agents);        // Identify overloaded and underloaded agents    const { overloaded, underloaded } = this.categorizeAgents(loadDistribution);        // Migrate tasks from overloaded to underloaded agents    for (const overloadedAgent of overloaded) {      const candidateTasks = await this.getMovableTasks(overloadedAgent.id);      const targetAgent = this.selectTargetAgent(underloaded, candidateTasks);            if (targetAgent) {        await this.migrateTasks(candidateTasks, overloadedAgent.id, targetAgent.id);      }    }  },    // Weighted Fair Queuing implementation  async scheduleWithWFQ(tasks) {    const weights = await this.calculateAgentWeights();    const virtualTimes = new Map();        return tasks.sort((a, b) => {      const aFinishTime = this.calculateFinishTime(a, weights, virtualTimes);      const bFinishTime = this.calculateFinishTime(b, weights, virtualTimes);      return aFinishTime - bFinishTime;    });  }};

// Advanced queue management systemclass PriorityTaskQueue {  constructor() {    this.queues = {      critical: new PriorityQueue((a, b) => a.deadline - b.deadline),      high: new PriorityQueue((a, b) => a.priority - b.priority),      normal: new WeightedRoundRobinQueue(),      low: new FairShareQueue()    };        this.schedulingWeights = {      critical: 0.4,      high: 0.3,      normal: 0.2,      low: 0.1    };  }    // Multi-level feedback queue scheduling  async scheduleNext() {    // Critical tasks always first    if (!this.queues.critical.isEmpty()) {      return this.queues.critical.dequeue();    }        // Use weighted scheduling for other levels    const random = Math.random();    let cumulative = 0;        for (const [level, weight] of Object.entries(this.schedulingWeights)) {      cumulative += weight;      if (random <= cumulative && !this.queues[level].isEmpty()) {        return this.queues[level].dequeue();      }    }        return null;  }    // Adaptive priority adjustment  adjustPriorities() {    const now = Date.now();        // Age-based priority boosting    for (const queue of Object.values(this.queues)) {      queue.forEach(task => {        const age = now - task.submissionTime;        if (age > this.agingThreshold) {          task.priority += this.agingBoost;        }      });    }  }}

// Intelligent resource allocationconst resourceAllocator = {  // Multi-objective optimization  async optimizeAllocation(agents, tasks, constraints) {    const objectives = [      this.minimizeLatency,      this.maximizeUtilization,      this.balanceLoad,      this.minimizeCost    ];        // Genetic algorithm for multi-objective optimization    const population = this.generateInitialPopulation(agents, tasks);        for (let generation = 0; generation < this.maxGenerations; generation++) {      const fitness = population.map(individual =>         this.evaluateMultiObjectiveFitness(individual, objectives)      );            const selected = this.selectParents(population, fitness);      const offspring = this.crossoverAndMutate(selected);      population.splice(0, population.length, ...offspring);    }        return this.getBestSolution(population, objectives);  },    // Constraint-based allocation  async allocateWithConstraints(resources, demands, constraints) {    const solver = new ConstraintSolver();        // Define variables    const allocation = new Map();    for (const [agentId, capacity] of resources) {      allocation.set(agentId, solver.createVariable(0, capacity));    }        // Add constraints    constraints.forEach(constraint => solver.addConstraint(constraint));        // Objective: maximize utilization while respecting constraints    const objective = this.createUtilizationObjective(allocation);    solver.setObjective(objective, 'maximize');        return await solver.solve();  }};

// MCP performance tools integrationconst mcpIntegration = {  // Real-time metrics collection  async collectMetrics() {    const metrics = await mcp.performance_report({ format: 'json' });    const bottlenecks = await mcp.bottleneck_analyze({});    const tokenUsage = await mcp.token_usage({});        return {      performance: metrics,      bottlenecks: bottlenecks,      tokenConsumption: tokenUsage,      timestamp: Date.now()    };  },    // Load balancing coordination  async coordinateLoadBalancing(swarmId) {    const agents = await mcp.agent_list({ swarmId });    const metrics = await mcp.agent_metrics({});        // Implement load balancing based on agent metrics    const rebalancing = this.calculateRebalancing(agents, metrics);        if (rebalancing.required) {      await mcp.load_balance({        swarmId,        tasks: rebalancing.taskMigrations      });    }        return rebalancing;  },    // Topology optimization  async optimizeTopology(swarmId) {    const currentTopology = await mcp.swarm_status({ swarmId });    const optimizedTopology = await this.calculateOptimalTopology(currentTopology);        if (optimizedTopology.improvement > 0.1) { // 10% improvement threshold      await mcp.topology_optimize({ swarmId });      return optimizedTopology;    }        return null;  }};

class EDFScheduler {  schedule(tasks) {    return tasks.sort((a, b) => a.deadline - b.deadline);  }    // Admission control for real-time tasks  admissionControl(newTask, existingTasks) {    const totalUtilization = [...existingTasks, newTask]      .reduce((sum, task) => sum + (task.executionTime / task.period), 0);        return totalUtilization <= 1.0; // Liu & Layland bound  }}

class CFSScheduler {  constructor() {    this.virtualRuntime = new Map();    this.weights = new Map();    this.rbtree = new RedBlackTree();  }    schedule() {    const nextTask = this.rbtree.minimum();    if (nextTask) {      this.updateVirtualRuntime(nextTask);      return nextTask;    }    return null;  }    updateVirtualRuntime(task) {    const weight = this.weights.get(task.id) || 1;    const runtime = this.virtualRuntime.get(task.id) || 0;    this.virtualRuntime.set(task.id, runtime + (1000 / weight)); // Nice value scaling  }}

class CircuitBreaker {  constructor(threshold = 5, timeout = 60000) {    this.failureThreshold = threshold;    this.timeout = timeout;    this.failureCount = 0;    this.lastFailureTime = null;    this.state = 'CLOSED'; // CLOSED, OPEN, HALF_OPEN  }    async execute(operation) {    if (this.state === 'OPEN') {      if (Date.now() - this.lastFailureTime > this.timeout) {        this.state = 'HALF_OPEN';      } else {        throw new Error('Circuit breaker is OPEN');      }    }        try {      const result = await operation();      this.onSuccess();      return result;    } catch (error) {      this.onFailure();      throw error;    }  }    onSuccess() {    this.failureCount = 0;    this.state = 'CLOSED';  }    onFailure() {    this.failureCount++;    this.lastFailureTime = Date.now();        if (this.failureCount >= this.failureThreshold) {      this.state = 'OPEN';    }  }}

// Load balancer benchmarking suiteconst 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    };  }};