此处仅贴出了贪心调度任务的实现代码。(来源云计算第二版)
package org.cloudbus.cloudsim.timeAware; import java.util.ArrayList; import java.util.Collections; import java.util.Comparator; import org.cloudbus.cloudsim.Cloudlet; import org.cloudbus.cloudsim.DatacenterBroker; import org.cloudbus.cloudsim.Vm; import org.cloudbus.cloudsim.lists.VmList; public class TimeAwarebroker extends DatacenterBroker{ public TimeAwarebroker(String name) throws Exception { super(name); } public void bindCloudletsToVmsTimeAwared(){ int cloudletNum = cloudletList.size(); int vmNum = vmList.size(); double[][] time = new double[cloudletNum][vmNum]; // 重新排列任务和虚拟机， 需要导入包java.util.Collections Collections.sort(cloudletList, new CloudletComparator()); System.out.println("测试——1"); for (int i=0; i<cloudletNum; i++){ System.out.println("cloudLet_ID:"+cloudletList.get(i).getCloudletId()+"--"+"Length:"+cloudletList.get(i).getCloudletLength()+" "); } for (int j=0; j<vmNum; j++){ System.out.println("vm_ID:"+vmList.get(j).getId()+"--"+"mips:"+vmList.get(j).getMips()+" "); } Collections.sort(vmList, new VmListComparator()); // 初始化矩阵 time for (int i = 0; i < cloudletNum; i++){ for (int j = 0; j < vmNum; j++){ time[i][j] = (double)cloudletList.get(i).getCloudletLength() / vmList.get(j).getMips(); System.out.println("(" + cloudletList.get(i).getCloudletId() + "):" + cloudletList.get(i).getCloudletLength() + "/" + vmList.get(j).getMips() + "=" + time[i][j]); } System.out.println(); } double[] vmLoad = new double[vmNum]; // 某个虚拟机上任务的总执行时间 int[] vmTasks = new int[vmNum]; // 某个虚拟机上运行的任务数 double minLoad = 0; // 记录当前任务分配方式的最优值 int idx = 0; // 记录当前任务最优分配方式对应的虚拟机号 // 将行号为 0 的任务直接分配给列号最大的虚拟机 vmLoad[vmNum-1] = time[0][vmNum-1]; vmTasks[vmNum-1] = 1; cloudletList.get(0).setVmId(vmList.get(vmNum-1).getId()); System.out.println("cloudLet_ID:"+cloudletList.get(0).getCloudletId()+"--"+"Length:"+cloudletList.get(0).getCloudletLength()+"--" + "VMID:" + cloudletList.get(0).getVmId()); for (int i = 1; i < cloudletNum; i++){ minLoad = vmLoad[vmNum-1] + time[i][vmNum-1]; idx = vmNum - 1; for (int j = vmNum - 2; j >= 0; j--){ // 如果当前虚拟机还未分配任务，则比较当前任务 // 分配给该虚拟机是否最优，即可以退出循环 if (vmLoad[j] == 0){ if (minLoad >= time[i][j]){ idx = j; break; } } // 如果当前虚拟机分配了任务，比较待分配任务分配到此虚拟机上是否为新的最优 if (minLoad > vmLoad[j] + time[i][j]){ minLoad = vmLoad[j] + time[i][j]; idx = j; } // 实现简单的负载均衡 // 如果时间一样，则分配给任务较少的虚拟机 else if (minLoad == vmLoad[j] + time[i][j] && vmTasks[j] < vmTasks[idx]){ idx = j; } vmLoad[idx] += time[i][idx]; vmTasks[idx]++; cloudletList.get(i).setVmId(vmList.get(idx).getId()); } } } //根据指令长度降序排列任务 private class CloudletComparator implements Comparator<Cloudlet>{ public int compare(Cloudlet cl1, Cloudlet cl2){ return (int)(cl2.getCloudletLength() - cl1.getCloudletLength()); } } //根据执行速度升序排列虚拟机 private class VmListComparator implements Comparator<Vm>{ public int compare(Vm vm1, Vm vm2){ return (int)(vm1.getMips() - vm2.getMips()); } } }