C++多線程在Linux下如何實(shí)現(xiàn)負(fù)載均衡

在Linux下，C++多線程可以通過(guò)多種方式實(shí)現(xiàn)負(fù)載均衡。以下是一些常見(jiàn)的方法：

1. 使用線程池

線程池是一種管理線程的機(jī)制，可以有效地分配任務(wù)給線程池中的線程，從而實(shí)現(xiàn)負(fù)載均衡。

#include <iostream>
#include <vector>
#include <queue>
#include <thread>
#include <mutex>
#include <condition_variable>
#include <functional>

class ThreadPool {
public:
    ThreadPool(size_t numThreads) : stop(false) {
        for (size_t i = 0; i < numThreads; ++i) {
            workers.emplace_back([this] {
                for (;;) {
                    std::function<void()> task;
                    {
                        std::unique_lock<std::mutex> lock(this->queueMutex);
                        this->condition.wait(lock, [this] { return this->stop || !this->tasks.empty(); });
                        if (this->stop && this->tasks.empty()) {
                            return;
                        }
                        task = std::move(this->tasks.front());
                        this->tasks.pop();
                    }
                    task();
                }
            });
        }
    }

    ~ThreadPool() {
        {
            std::unique_lock<std::mutex> lock(queueMutex);
            stop = true;
        }
        condition.notify_all();
        for (std::thread &worker : workers) {
            worker.join();
        }
    }

    template <class F, class... Args>
    void enqueue(F&& f, Args&&... args) {
        {
            std::unique_lock<std::mutex> lock(queueMutex);
            if (stop) {
                throw std::runtime_error("enqueue on stopped ThreadPool");
            }
            tasks.emplace([f, args...] { f(args...); });
        }
        condition.notify_one();
    }

private:
    std::vector<std::thread> workers;
    std::queue<std::function<void()>> tasks;
    std::mutex queueMutex;
    std::condition_variable condition;
    bool stop;
};

void worker(int id) {
    std::cout << "Worker " << id << " started\n";
    // Simulate work
    std::this_thread::sleep_for(std::chrono::seconds(1));
    std::cout << "Worker " << id << " finished\n";
}

int main() {
    ThreadPool pool(4);
    for (int i = 0; i < 10; ++i) {
        pool.enqueue(worker, i);
    }
    return 0;
}

2. 使用任務(wù)隊(duì)列

任務(wù)隊(duì)列是一種將任務(wù)分配給多個(gè)線程的簡(jiǎn)單方法。每個(gè)線程從隊(duì)列中獲取任務(wù)并執(zhí)行。

#include <iostream>
#include <vector>
#include <queue>
#include <thread>
#include <mutex>
#include <condition_variable>
#include <functional>

class TaskQueue {
public:
    void push(std::function<void()> task) {
        std::lock_guard<std::mutex> lock(mutex);
        tasks.push(task);
        condition.notify_one();
    }

    std::function<void()> pop() {
        std::unique_lock<std::mutex> lock(mutex);
        condition.wait(lock, [this] { return !tasks.empty(); });
        auto task = tasks.front();
        tasks.pop();
        return task;
    }

private:
    std::queue<std::function<void()>> tasks;
    std::mutex mutex;
    std::condition_variable condition;
};

void worker(TaskQueue& queue) {
    while (true) {
        auto task = queue.pop();
        if (task == nullptr) {
            break;
        }
        task();
    }
}

int main() {
    TaskQueue queue;
    std::vector<std::thread> workers;

    for (int i = 0; i < 4; ++i) {
        workers.emplace_back(worker, std::ref(queue));
    }

    for (int i = 0; i < 10; ++i) {
        queue.push([i] { std::cout << "Task "<< i << " started\n"; });
    }

    for (auto& worker : workers) {
        worker.join();
    }

    return 0;
}

3. 使用工作竊取算法

工作竊取算法是一種動(dòng)態(tài)負(fù)載均衡策略，適用于多處理器系統(tǒng)。每個(gè)線程都有一個(gè)本地任務(wù)隊(duì)列，當(dāng)一個(gè)線程的任務(wù)隊(duì)列為空時(shí)，它會(huì)嘗試從其他線程的隊(duì)列中竊取任務(wù)。

#include <iostream>
#include <vector>
#include <queue>
#include <thread>
#include <mutex>
#include <condition_variable>
#include <functional>

class Worker {
public:
    Worker(int id, TaskQueue& globalQueue) : id(id), globalQueue(globalQueue) {}

    void run() {
        while (true) {
            std::function<void()> task;
            {
                std::unique_lock<std::mutex> lock(queueMutex);
                condition.wait(lock, [this] { return !tasks.empty() || globalQueue.empty(); });
                if (globalQueue.empty() && tasks.empty()) {
                    return;
                }
                if (!globalQueue.empty()) {
                    task = std::move(globalQueue.front());
                    globalQueue.pop();
                } else {
                    task = std::move(tasks.front());
                    tasks.pop();
                }
            }
            task();
        }
    }

    void addTask(std::function<void()> task) {
        {
            std::lock_guard<std::mutex> lock(queueMutex);
            tasks.push(task);
        }
        condition.notify_one();
    }

private:
    int id;
    std::queue<std::function<void()>> tasks;
    std::mutex queueMutex;
    std::condition_variable condition;
    TaskQueue& globalQueue;
};

int main() {
    TaskQueue globalQueue;
    std::vector<Worker> workers;

    for (int i = 0; i < 4; ++i) {
        workers.emplace_back(i, std::ref(globalQueue));
    }

    for (int i = 0; i < 10; ++i) {
        globalQueue.push([i] { std::cout << "Task "<< i << " started\n"; });
    }

    for (auto& worker : workers) {
        worker.run();
    }

    return 0;
}

這些方法都可以在Linux下實(shí)現(xiàn)C++多線程的負(fù)載均衡。選擇哪種方法取決于具體的應(yīng)用場(chǎng)景和需求。