sort_by_disk.py 8.8 KB

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  1. #!/usr/bin/env python
  2. # -*- coding: utf-8 -*-
  3. '''
  4. 按照磁盘占用率从大到小装箱,即按照磁盘先用完为止进行分配实例到主机。
  5. @Auther :liuyuqi.gov@msn.cn
  6. @Time :2018/7/7 0:43
  7. @File :sort_by_disk.py
  8. '''
  9. import matplotlib
  10. matplotlib.use('Agg')
  11. import pandas as pd
  12. from configparser import ConfigParser
  13. import time
  14. import libs.save_result
  15. cf = ConfigParser()
  16. config_path = "../conf/config.ini"
  17. section_name = "data_file_name"
  18. cf.read(config_path)
  19. app_interference = cf.get(section_name, "app_interference")
  20. app_resources = cf.get(section_name, "app_resources")
  21. instance = cf.get(section_name, "instance")
  22. # app
  23. df1 = pd.read_csv(app_resources, encoding="utf-8")
  24. # instance
  25. df3 = pd.read_csv(instance)
  26. df3["cpu"] = df3["cpu"].astype("float")
  27. df3["disk"] = df3["disk"].astype("float")
  28. df3["mem"] = df3["mem"].astype("float")
  29. df3["M"] = df3["M"].astype("float")
  30. df3["P"] = df3["P"].astype("float")
  31. df3["PM"] = df3["PM"].astype("float")
  32. df3["isdeploy"] = False
  33. # machine
  34. # 其实就两类,所以就不需要导入数据了。
  35. # 限制表
  36. df4 = pd.read_csv(app_interference, header=None,
  37. names=list(["appid1", "appid2", "max_interference"]), encoding="utf-8")
  38. result = pd.DataFrame(columns=list(["instanceid", "machineid"]), data=list())
  39. tem_pre_disk = tem_pre_mem = tem_pre_cpu = tem_pre_P = tem_pre_M = tem_pre_PM = 0
  40. tem_disk = tem_mem = tem_cpu = tem_P = tem_M = tem_PM = 0
  41. tmp_stand_cpu1 = 32
  42. tmp_stand_mem1 = 64
  43. tmp_stand_disk1 = 600
  44. tmp_stand_cpu2 = 92
  45. tmp_stand_mem2 = 288
  46. tmp_stand_disk2 = 600
  47. tmp_stand_P = 7
  48. tmp_stand_M1 = 3
  49. tmp_stand_M2 = 7
  50. tmp_stand_PM1 = 7
  51. tmp_stand_PM2 = 9
  52. machine_count = 0 # 3000小机器,3000大机器。所以在小机器用完换大机器
  53. j = 1 # j表示主机序号,从1-3000,3001到6000
  54. is_deploy = False # 主机j是否部署了instance
  55. deploy_list = list() # 主机j部署的instanceid实例
  56. # 各app之间的限制
  57. def restrictApps(instance, deploy_list):
  58. len_list = len(deploy_list)
  59. if len_list == 0:
  60. return True
  61. else:
  62. ct = pd.Series(deploy_list).value_counts()
  63. for k, v in ct.items():
  64. # tmp表示在df4中找到限制条件一行
  65. tmp = df4.loc[(df4["appid1"] == k) & (df4["appid2"] == instance)]
  66. row, col = tmp.shape
  67. if k == instance:
  68. # a a 2 表示有一个a前提,还可以放2个a,最多可以放3个a
  69. if row > 0:
  70. if ct[instance] > tmp["max_interference"]:
  71. return False
  72. else:
  73. # a b 2 表示有一个a前提,还可以放2个b,最多可以放2个b
  74. if row > 0:
  75. if ct[instance] + 1 > tmp["max_interference"]:
  76. return False
  77. return True
  78. # 执行部署方案
  79. def deploy():
  80. global j, is_deploy, tem_mem, tem_cpu, tem_disk, tem_P, tem_M, tem_PM, tem_pre_disk, tem_pre_mem, \
  81. tem_pre_cpu, tem_pre_P, tem_pre_M, tem_pre_PM, result, df3, deploy_list
  82. print("------------开始部署啦--------------")
  83. start = time.time()
  84. row, column = df3.shape
  85. while row > 0:
  86. deployInstance()
  87. # 整个instace都遍历了,第j主机无法再放入一个,所以添加j+1主机
  88. df3 = df3[df3["isdeploy"] == False]
  89. row, column = df3.shape
  90. df3 = df3.reset_index(drop=True)
  91. j = j + 1
  92. # j++之后表示新建主机,所以新主机没有部署任何实例,为false,然后初始化所有其他参数
  93. is_deploy = False
  94. tem_pre_disk = tem_pre_mem = tem_pre_cpu = tem_pre_P = tem_pre_M = tem_pre_PM = 0
  95. tem_disk = tem_mem = tem_cpu = tem_P = tem_M = tem_PM = 0
  96. deploy_list = list()
  97. # 部署完事
  98. print("------------部署完啦--------------")
  99. end = time.time()
  100. print("总共耗时:", end - start, "秒")
  101. print("总共需要主机数:", j)
  102. print("部署方案前几条示意:", result.head())
  103. libs.save_result.save_result(result)
  104. def deployInstance():
  105. '''
  106. 根据限制部署实例到主机上
  107. :param row: 根据剩余的instance数量循环
  108. :param j: 第j台主机
  109. :return: 暂未定返回值,None
  110. '''
  111. global is_deploy, tem_mem, tem_cpu, tem_disk, tem_P, tem_M, tem_PM, tem_pre_disk, tem_pre_mem, tem_pre_cpu, tem_pre_P, tem_pre_M, tem_pre_PM, result, j, df3, deploy_list
  112. for row in df3.itertuples():
  113. i = row.Index
  114. tem_pre_cpu = tem_cpu + row.cpu
  115. tem_pre_mem = tem_mem + row.mem
  116. tem_pre_disk = tem_disk + row.disk # 当前磁盘消耗
  117. tem_pre_P = tem_P + row.P
  118. tem_pre_M = tem_M + row.M
  119. tem_pre_PM = tem_PM + row.PM
  120. # if 满足限制表条件,则把当前实例部署到这台主机上。
  121. if j < 3000: # 使用小主机
  122. if is_deploy == True:
  123. if tem_pre_disk < tmp_stand_disk1: # 磁盘够
  124. if restrictApps(instance=row.instanceid, deploy_list=deploy_list):
  125. if tem_pre_mem < tmp_stand_mem1: # 内存够
  126. if tem_pre_cpu < tmp_stand_cpu1: # CPU够
  127. if tem_pre_M < tmp_stand_M1:
  128. if tem_pre_P < tmp_stand_P:
  129. if tem_pre_PM < tmp_stand_PM1:
  130. # 条件都满足,则把instance放入主机,同时df3表中去掉这个部署好的一行
  131. result = result.append(pd.DataFrame(
  132. [{"instanceid": row.instanceid,
  133. "machineid": "machine_" + str(j)}]))
  134. tem_disk = tem_disk + row.disk
  135. tem_mem = tem_mem + row.mem
  136. tem_cpu = tem_cpu + row.cpu
  137. tem_P = tem_P + row.P
  138. tem_M = tem_M + row.M
  139. tem_PM = tem_PM + row.PM
  140. df3.loc[i, "isdeploy"] = True
  141. deploy_list.append(row.instanceid)
  142. else:
  143. # 主机j没有部署实例,则先部署一个
  144. result = result.append(
  145. pd.DataFrame([{"instanceid": row.instanceid, "machineid": "machine_" + str(j)}]))
  146. tem_disk = tem_disk + row.disk
  147. tem_mem = tem_mem + row.mem
  148. tem_cpu = tem_cpu + row.cpu
  149. tem_P = tem_P + row.P
  150. tem_M = tem_M + row.M
  151. tem_PM = tem_PM + row.PM
  152. df3.loc[i, "isdeploy"] = True
  153. deploy_list.append(row.instanceid)
  154. # df3["isdeploy"][i] = True
  155. is_deploy = True
  156. else: # 使用大主机
  157. if is_deploy == True:
  158. if tem_pre_disk < tmp_stand_disk2: # 磁盘够
  159. if restrictApps(instance=row.instanceid, deploy_list=deploy_list):
  160. if tem_pre_mem < tmp_stand_mem2: # 内存够
  161. if tem_pre_cpu < tmp_stand_cpu2: # CPU够
  162. if tem_pre_M < tmp_stand_M2:
  163. if tem_pre_P < tmp_stand_P:
  164. if tem_pre_PM < tmp_stand_PM2:
  165. # 条件都满足,则把instance放入主机
  166. result = result.append(pd.DataFrame(
  167. [{"instanceid": row.instanceid,
  168. "machineid": "machine_" + str(j)}]))
  169. tem_disk = tem_disk + row.disk
  170. tem_mem = tem_mem + row.mem
  171. tem_cpu = tem_cpu + row.cpu
  172. tem_P = tem_P + row.P
  173. tem_M = tem_M + row.M
  174. tem_PM = tem_PM + row.PM
  175. df3.loc[i, "isdeploy"] = True
  176. deploy_list.append(row.instanceid)
  177. else:
  178. # 主机j没有部署实例,则先部署一个
  179. result = result.append(
  180. pd.DataFrame([{"instanceid": row.instanceid, "machineid": "machine_" + str(j)}]))
  181. tem_disk = tem_disk + row.disk
  182. tem_mem = tem_mem + row.mem
  183. tem_cpu = tem_cpu + row.cpu
  184. tem_P = tem_P + row.P
  185. tem_M = tem_M + row.M
  186. tem_PM = tem_PM + row.PM
  187. df3.loc[i, "isdeploy"] = True
  188. deploy_list.append(row.instanceid)
  189. is_deploy = True
  190. deploy()