java 与操作系统进程同步问题（一）————互斥问题

最近学校开设了操作系统原理课程，老师要求用任意语言去模拟进程的同步和互斥问题。

在尝试的写了之后，发现这个问题非常有意思，故想记录在博客中，作为自己的学习轨迹。

个人还是比较喜欢用Java语言，所以采用了java来编写。今天记录的是多个进程访问互斥资源量的问题，互斥即是某一资源同一时刻，只允许一个进程访问，在离散数学中，对互斥定义如下

事件A和B的交集为空，A与B就是互斥事件，也叫互不相容事件。也可叙述为：不可能同时发生的事件。如A∩B为不可能事件（A∩B=Φ），那么称事件A与事件B互斥，其含义是：事件A与事件B在任何一次试验中不会同时发生（百度百科）。

如日常生活中的打印机，就是一个公共资源，同一时刻，只允许一个任务进行，其他任务排队等待。

采用记录型信号量来实现。

相应的wait(Semaphore s) (wait操作就是p操作，我们的课本里是这种叫法)的伪代码就是

 wait(Semaphore *s){
      s->value--;　　　　　　　　　//value是资源个数
      if(s->value < ){
           block(s->list);       //list是PCB(process_control_block)块
      }
 }    

对应的signal（signal就是v操作）的伪代码就是:

signal（Semaphore *s）{
       s->value++;
       if(s->value >= )
             wakeup(s->list);
}

所以首先我们得实现一个信号量类，采用synchronized块来模拟wait和signal操作 ,具体synchronized的细节请自行百度或者查阅其他博客园的文章，我理解的也不是特别透彻。

public class Semaphore {
    public Object lock = new Object();　　　　　　　　　　//synchronized锁住的是对象
    public  int value;　　　　　　　　　　　　　　　　　　　//资源个数

    public Semaphore(int value) {
        this.value = value;
    }
}

在该类中我们实现wait和signal方法

    public static  void Wait(Semaphore semaphore,String className) {　　　　　　　　//classname用来判断是那个线程
        synchronized (semaphore.lock) {
            semaphore.value--;
            if (semaphore.value < 0)
            {
                try {
                    System.out.println(className + "被阻塞");
                    semaphore.lock.wait();

                } catch (InterruptedException e) {
                    // TODO Auto-generated catch block
                    e.printStackTrace();
                }
            }
        }

    }

    public static  void Signal(Semaphore semaphore,String className){
        synchronized (semaphore.lock) {
            semaphore.value++;
            if (semaphore.value <= 0) {
                System.out.println(className + "资源量足够，唤醒一个");
                semaphore.lock.notify();
            }
        }

    }    

可以看到跟最开始的伪代码基本思想都是一样的。

到此，我们就已经完成了记录型信号量。

下面我们就用记录型信号量来完成互斥关系

首先给出用记录型信号量完成互斥关系的伪代码

operation()
{
     while(1){
        wait(mutex);
        //进入临界区
        signal(mutex);
        //剩余区
    }
}    

临界区：就是进程中访问临界资源的代码

我们模拟多个人使用打印机来模拟进程互斥问题。

一般解决同步问题要先确定信号量，互斥信号量非常好确定，就是打印机，我们可以设初始打印机资源为1

首先完成一个Runnable的子类，它就是对打印机进行的操作，即为上述的operation

public class PrinterUser implements Runnable{
    //打印机信号量
    Semaphore printer;
　　//确认线程身份
    String userName;
    public PrinterUser(Semaphore printer,String userName) {
        // TODO 自动生成的构造函数存根
        this.printer = printer;
        this.userName = userName;
    }
    @Override
    public void run() {
        // TODO 自动生成的方法存根
        while(true){
            Semaphore.Wait(printer, userName);
            System.out.println("正在打印");
            Semaphore.Signal(printer, userName);
            System.out.println(userName+"打印完成");
        }
    }

}

给出测试

public static void main(String[] args) {
        Semaphore printer = new Semaphore(1);
        // TODO Auto-generated method stub
        Thread threada= new Thread(new PrinterUser(printer, "打印者1"));
        Thread threadb= new Thread(new PrinterUser(printer, "打印者2"));

        threada.start();threadb.start();
    }

测试结果

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" 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