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task.py
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task.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Apr 23 10:34:15 2020
对整体进行调度，已知每个作业的运行时长
@author: Wang
"""
import numpy as np
def currentPriority(waitList,currentTime,accessNodeNum,weight,waitNum):
# 找出当前时刻及其之前的作业
priority=np.zeros((1,waitNum),dtype=float)
index=np.where(waitList[:,4]<=currentTime)[0]
if len(index)>0:
compareList=waitList[index,:]
temp=weight[0]*compareList[:,1]/(1+sum(compareList[:,1]))+\
weight[1]*(1-compareList[:,2]/max(compareList[:,2]))+\
weight[2]*compareList[:,3]/(1+sum(compareList[:,3]))+\
weight[3]*(1-compareList[:,4]/(1+max(compareList[:,4])))+\
weight[4]*compareList[:,5]/(1+sum(compareList[:,5]))+\
weight[5]*(1-(abs(compareList[:,6]-accessNodeNum)/max(abs(compareList[:,6]-accessNodeNum))))+\
weight[6]*compareList[:,7]/(1+sum(compareList[:,7]))
priority[0,index]=temp
return priority
def plotTake(result):
import matplotlib.pyplot as plt
import numpy as np
color=['r','g','b','m']
fig = plt.figure()
for i in range(len(result)):
for j in result[i][12]:
plt.plot(np.array([result[i][9],result[i][10]]),np.array([j,j]),color[i%4])
plt.plot(np.array([result[i][9],result[i][9]]),np.array([j,j+1]),color[i%4])
plt.plot(np.array([result[i][9],result[i][10]]),np.array([j+1,j+1]),color[i%4])
plt.plot(np.array([result[i][10],result[i][10]]),np.array([j,j+1]),color[i%4])
plt.text(np.max([result[i][9],(result[i][10]+result[i][9])/2]),j+0.5,str(result[i][0]))
plt.xlabel('Time')
plt.ylabel('Node')
plt.title("Job Scheduling")
plt.show()
nodeNum=10 # 节点的总数目
currentTime=0 # 当前时刻, 以第一个作业提交时刻为初始0点
weight=[1,1,1,1,1,1,1] # 权重系数
waitList=np.array([(0,1,1,0,0,0,2,0,1),
(1,1,1,0,0,0,3,0,1),
(2,1,2,0,5,0,3,0,1),
(3,2,1,0,5,0,2,0,2),
(4,2,1,0,5,0,4,0,2),
(5,1,1,0,10,0,2,0,3),
(6,3,1,0,10,0,3,0,3),
(7,2,1,0,20,0,3,0,3)])
waitNum=waitList.shape[0] # 等待列表作业数目
# 0作业ID 1用户等级 2用户提交次数 3已使用时长 4作业提交时刻 5被抢占个数 6所需节点
# 7等待时长 8所属用户
# 计算当前阶段的优先级
userType=np.unique(waitList[:,8]) # 用户类型
# 随机产生每个任务的实际完成时间
randFinishTime=waitList[:,4]+np.random.randint(10,50,waitNum)
randFinishTime=np.array([26, 36, 41, 48, 50, 50, 60, 70])
tim=randFinishTime
TimeTable=np.zeros((nodeNum,10*max(randFinishTime)),dtype=int)
TimeTable2=np.zeros((nodeNum,10*max(randFinishTime)),dtype=int)
result=[]
while waitNum:
accessNodeNum=len(np.where(TimeTable[:,currentTime]==0)[0]) # 找出空闲节点
if len(np.where(waitList[:,6]<=accessNodeNum)[0])>0:
priority=currentPriority(waitList,currentTime,accessNodeNum,weight,waitNum)
sortIndex=np.argsort(-priority) # 获取降序索引
waitList=waitList[sortIndex[0],:]
randFinishTime=randFinishTime[sortIndex[0]]
# 判断当前时刻是否存在最优任务可用的节点
while waitList[0,4]<=currentTime: # 所调度的作业必须已
fassibleIndex=np.where(TimeTable[:,currentTime]==0)[0] # 找出空闲节点
if len(fassibleIndex)>=waitList[0,6]: # 如果可用节点数目满足
TimeTable[fassibleIndex[0:waitList[0,6]],currentTime:randFinishTime[0]]=1
TimeTable2[fassibleIndex[0:waitList[0,6]],currentTime:randFinishTime[0]]=100+waitList[0,0]
temp_result=list(waitList[0,:]) # 0-8
temp_result.append(currentTime) # 9实际开始时间
temp_result.append(randFinishTime[0]) # 10实际结束时间
temp_result.append(currentTime-waitList[0,4]) # 11延迟时间
temp_result.append(fassibleIndex[0:waitList[0,6]]) # 12所分配的节点
result.append(temp_result)
# 更新被抢占次数
stopIndex=1+np.where(waitList[1:,4]<=waitList[0,4])[0]
waitList[stopIndex,5]+=1 # 时间提的早却没分上
waitList=np.copy(waitList[1:,:]) # 将已放置的删除
randFinishTime=np.copy(randFinishTime[1:])
waitNum-=1 # 更新队列数目
if waitNum==0:
break
else: # 更新各个任务的状态
break # 没有满足数目的节点
plotTake(result)
# 更新列表中的其他作业信息
finishTime=0 # 计算至当前时刻，用户所有所用的时间
for i in userType:
for value in result:
if value[8]==i: # 同一用户
finishTime+=currentTime-value[9]
sameUser=1+np.where(waitList[1:,8]==i)[0]
waitList[sameUser,3]=finishTime
currentTime+=1
waitList[:,7]=currentTime-waitList[:,4] # 更新等待时长
errIndex=np.where(waitList[:,7]<0)
waitList[errIndex,7]=0
print(currentTime)
plotTake(result)