11.11智能批量计算推荐

def recommend_resources(history_tasks, current_task):
    # 假设history_tasks是历史任务列表，每个任务包含资源使用信息
    # current_task是当前待分配的任务
    
    # 计算历史任务的平均资源使用情况
    avg_cpu = sum(task['cpu'] for task in history_tasks) / len(history_tasks)
    avg_memory = sum(task['memory'] for task in history_tasks) / len(history_tasks)
    
    # 根据当前任务的复杂度调整推荐资源
    recommended_cpu = avg_cpu * (current_task['complexity'] / 10)
    recommended_memory = avg_memory * (current_task['complexity'] / 10)
    
    return {
        'cpu': recommended_cpu,
        'memory': recommended_memory
    }

# 示例使用
history_tasks = [
    {'cpu': 4, 'memory': 8, 'complexity': 5},
    {'cpu': 6, 'memory': 12, 'complexity': 7}
]
current_task = {'complexity': 8}

recommendation = recommend_resources(history_tasks, current_task)
print("Recommended Resources:", recommendation)