使用Cloudsim实现基于多维QoS的资源调度算法代码

以下是使用CloudSim实现基于多维QoS的资源调度算法的示例代码：

import org.cloudbus.cloudsim.Cloudlet;
import org.cloudbus.cloudsim.CloudletSchedulerSpaceShared;
import org.cloudbus.cloudsim.Datacenter;
import org.cloudbus.cloudsim.DatacenterBroker;
import org.cloudbus.cloudsim.DatacenterCharacteristics;
import org.cloudbus.cloudsim.Host;
import org.cloudbus.cloudsim.Pe;
import org.cloudbus.cloudsim.Storage;
import org.cloudbus.cloudsim.Vm;
import org.cloudbus.cloudsim.VmAllocationPolicySimple;
import org.cloudbus.cloudsim.core.CloudSim;
import java.util.ArrayList;
import java.util.Calendar;
import java.util.List;

public class QoSResourceScheduling {

    public static void main(String[] args) {
        int numBrokers = 1; // 创建一个代理商
        Calendar calendar = Calendar.getInstance();
        boolean traceFlag = false; // 关闭日志跟踪
        CloudSim.init(numBrokers, calendar, traceFlag);

        Datacenter datacenter = createDatacenter("Datacenter_0");
        DatacenterBroker broker = createBroker();
        int brokerId = broker.getId();

        List<Vm> vms = createVms(brokerId);
        List<Cloudlet> cloudlets = createCloudlets(brokerId);

        broker.submitVmList(vms);
        broker.submitCloudletList(cloudlets);

        CloudSim.startSimulation();
        List<Cloudlet> finishedCloudlets = broker.getCloudletReceivedList();
        CloudSim.stopSimulation();

        printCloudletResults(finishedCloudlets);
    }

    private static Datacenter createDatacenter(String name) {
        List<Host> hostList = new ArrayList<Host>();
        List<Pe> peList = new ArrayList<Pe>();
        int mips = 1000;
        peList.add(new Pe(0, new PeProvisionerSimple(mips)));

        int hostId = 0;
        int ram = 4096; // 主机内存（以MB为单位）
        long storage = 1000000; // 主机存储容量（以MB为单位）
        int bw = 10000; // 主机带宽
        hostList.add(new Host(hostId, new RamProvisionerSimple(ram), new BwProvisionerSimple(bw), storage, peList, new VmSchedulerSpaceShared(peList)));

        String arch = "x86"; // 主机架构
        String os = "Linux"; // 主机操作系统
        String vmm = "Xen"; // 主机监视程序
        double time_zone = 10.0; // 主机时区
        double cost = 3.0; // 主机每秒的成本
        double costPerMem = 0.05; // 主机每MB内存的成本
        double costPerStorage = 0.1; // 主机每MB存储的成本
        double costPerBw = 0.1; // 主机每Mbps带宽的成本
        LinkedList<Storage> storageList = new LinkedList<Storage>();

        DatacenterCharacteristics characteristics = new DatacenterCharacteristics(arch, os, vmm, hostList, time_zone, cost, costPerMem, costPerStorage, costPerBw);
        Datacenter datacenter = null;
        try {
            datacenter = new Datacenter(name, characteristics, new VmAllocationPolicySimple(hostList), storageList, 0);
        } catch (Exception e) {
            e.printStackTrace();
        }
        return datacenter;
    }

    private static DatacenterBroker createBroker() {
        DatacenterBroker broker = null;
        try {
            broker = new DatacenterBroker("Broker");
        } catch (Exception e) {
            e.printStackTrace();
            return null;
        }
        return broker;
    }

    private static List<Vm> createVms(int brokerId) {
        List<Vm> vms = new ArrayList<Vm>();
        int vmId = 0;
        int mips = 1000;
        int size = 10000; // 虚拟机大小（以字节为单位）
        int ram = 512; // 虚拟机内存（以MB为单位）
        long bw = 1000; // 虚拟机带宽
        int pesNumber = 1; // 虚拟机处理单元数量
        String vmm = "Xen"; // 虚拟机监视程序

        for (int i = 0; i < 10; i++) {
            Vm vm = new Vm(vmId, brokerId, mips, pesNumber, ram, bw, size, vmm, new CloudletSchedulerSpaceShared());
            vms.add(vm);
            vmId++;
        }
        return vms;
    }

    private static List<Cloudlet> createCloudlets(int brokerId) {
        List<Cloudlet> cloudlets = new ArrayList<Cloudlet>();
        int cloudletId = 0;
        long length = 40000; // 执行云任务所需的MI数
        long fileSize = 300; // 输入文件大小（以字节为单位）
        long outputSize = 300; // 输出文件大小（以字节为单位）
        UtilizationModel utilizationModel = new UtilizationModelFull();

        for (int i = 0; i < 10; i++) {
            Cloudlet cloudlet = new Cloudlet(cloudletId, length, 1, fileSize, outputSize, utilizationModel, utilizationModel, utilizationModel);
            cloudlet.setUserId(brokerId);
            cloudlets.add(cloudlet);
            cloudletId++;
        }
        return cloudlets;
    }

    private static void printCloudletResults(List<Cloudlet> cloudlets) {
        System.out.println("Cloudlet ID\tStatus\tData center ID\tVM ID\tTime\tStart Time\tFinish Time");
        for (Cloudlet cloudlet : cloudlets) {
            System.out.println(cloudlet.getCloudletId() + "\t" + cloudlet.getStatusString() + "\t" + cloudlet.getResourceId() + "\t" +
                    cloudlet.getVmId() + "\t" + cloudlet.getActualCPUTime() + "\t" + cloudlet.getExecStartTime() + "\t" + cloudlet.getFinishTime());
        }
    }
}

这是一个基于多维QoS的资源调度算法的简单示例代码。你可以根据自己的需求进行调整和扩展。希望对你有所帮助！