#!/usr/bin/env python # -*- coding: utf-8 -*- ''' 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。 @Auther :liuyuqi.gov@msn.cn @Time :2018/7/7 0:43 @File :sort_by_disk.py ''' import matplotlib matplotlib.use('Agg') import pandas as pd import matplotlib.pyplot as plt from configparser import ConfigParser import time import libs.save_result # A value is trying to be set on a copy of a slice from a DataFrame. pd.set_option('mode.chained_assignment', 'raise') # sys.path.append("/home/ubuntu/ServerManager") cf = ConfigParser() config_path = "../conf/config.ini" section_name = "data_file_name" cf.read(config_path) app_interference = cf.get(section_name, "app_interference") app_resources = cf.get(section_name, "app_resources") instance = cf.get(section_name, "instance") # app df1 = pd.read_csv(app_resources, encoding="utf-8") # instance df3 = pd.read_csv(instance) print(df3["cpu"][0]) # print(df3["cpu"].value_counts()) # print(df3.head()) df3["cpu"] = df3["cpu"].astype("float") df3["disk"] = df3["disk"].astype("float") df3["mem"] = df3["mem"].astype("float") df3["M"] = df3["M"].astype("float") df3["P"] = df3["P"].astype("float") df3["PM"] = df3["PM"].astype("float") df3["isdploy"] = False # machine # 其实就两类，所以就不需要导入数据了。 # 限制表 df4 = pd.read_csv(app_interference, header=None, names=list(["appid1", "appid2", "max_interference"]), encoding="utf-8") result = pd.DataFrame(columns=list(["instanceid", "machineid"]), data=list()) tem_pre_disk = tem_pre_mem = tem_pre_cpu = tem_pre_P = tem_pre_M = tem_pre_PM = 0 tem_disk = tem_mem = tem_cpu = tem_P = tem_M = tem_PM = 0 tmp_stand_cpu1 = 32 tmp_stand_mem1 = 64 tmp_stand_disk1 = 600 tmp_stand_cpu2 = 92 tmp_stand_mem2 = 288 tmp_stand_disk2 = 600 tmp_stand_P = 7 tmp_stand_M1 = 3 tmp_stand_M2 = 7 tmp_stand_PM1 = 7 tmp_stand_PM2 = 9 machine_count = 0 # 3000小机器，3000大机器。所以在小机器用完换大机器 j = 1 # j表示主机序号，从1-3000，3001到6000 is_deploy = False # 主机j是否部署了instance deploy_list = list() # 主机j部署的instanceid实例 # 各app之间的限制 def restrictApps(instance, deploy_list): len_list = len(deploy_list) if len_list == 0: return True else: ct = pd.Series(deploy_list).value_counts() for k, v in ct.items(): tmp = df4.loc[(df4["appid1"] == k) & (df4["appid2"] == instance)] row, col = tmp.shape if row > 0: if ct[instance] + 1 > tmp["max_interference"]: return False else: #在限制表中找不到限制条件 return True # 执行部署方案 def deplay(): global j, 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, df3, deploy_list print("------------开始部署啦--------------") start = time.time() row, column = df3.shape while row > 0: deployInstance(row) # 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 row, column = df3.shape j = j + 1 # j++之后表示新建主机，所以新主机没有部署任何实例，为false is_deploy = False tem_pre_disk = tem_pre_mem = tem_pre_cpu = tem_pre_P = tem_pre_M = tem_pre_PM = 0 tem_disk = tem_mem = tem_cpu = tem_P = tem_M = tem_PM = 0 df3 = df3[df3["isdeplay"] == False] deploy_list = list() # 部署完事 print("------------部署完啦--------------") end = time.time() print("总共耗时：", end - start, "秒") print("总共需要主机数：", j) print("部署方案前几条示意：", result.head()) libs.save_result.save_result(result) def deployInstance(row): ''' 根据限制部署实例到主机上 :param row: 根据剩余的instance数量循环 :param j: 第j台主机 :return: 暂未定返回值，None ''' 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 for i in range(0, row): tem_pre_cpu = tem_cpu + df3["cpu"][i] tem_pre_mem = tem_mem + df3["mem"][i] tem_pre_disk = tem_disk + df3["disk"][i] # 当前磁盘消耗 tem_pre_P = tem_P + df3["P"][i] tem_pre_M = tem_M + df3["M"][i] tem_pre_PM = tem_PM + df3["PM"][i] # if 满足限制表条件，则把当前实例部署到这台主机上。 if j < 3000: # 使用小主机 if is_deploy == True: if tem_pre_disk < tmp_stand_disk1: # 磁盘够 if restrictApps(instance=df3["instanceid"][i], deploy_list=deploy_list): if tem_pre_mem < tmp_stand_mem1: # 内存够 if tem_pre_cpu < tmp_stand_cpu1: # CPU够 if tem_pre_M < tmp_stand_M1: if tem_pre_P < tmp_stand_P: if tem_pre_PM < tmp_stand_PM1: # 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 result = result.append(pd.DataFrame( [{"instanceid": df3["instanceid"][i], "machineid": "machine_" + str(j)}])) tem_disk = tem_disk + df3["disk"][i] tem_mem = tem_mem + df3["mem"][i] tem_cpu = tem_cpu + df3["cpu"][i] tem_P = tem_P + df3["P"][i] tem_M = tem_M + df3["M"][i] tem_PM = tem_PM + df3["PM"][i] df3.loc[i, "isdploy"] = True deploy_list.append(df3["instanceid"][i]) else: # 主机j没有部署实例，则先部署一个 result = result.append( pd.DataFrame([{"instanceid": df3["instanceid"][i], "machineid": "machine_" + str(j)}])) tem_disk = tem_disk + df3["disk"][i] tem_mem = tem_mem + df3["mem"][i] tem_cpu = tem_cpu + df3["cpu"][i] tem_P = tem_P + df3["P"][i] tem_M = tem_M + df3["M"][i] tem_PM = tem_PM + df3["PM"][i] df3.loc[i, "isdploy"] = True deploy_list.append(df3["instanceid"][i]) # df3["isdploy"][i] = True is_deploy = True else: # 使用大主机 if is_deploy == True: if tem_pre_disk < tmp_stand_disk2: # 磁盘够 if restrictApps(instance=df3["instanceid"][i], deploy_list=deploy_list): if tem_pre_mem < tmp_stand_mem2: # 内存够 if tem_pre_cpu < tmp_stand_cpu2: # CPU够 if tem_pre_M < tmp_stand_M2: if tem_pre_P < tmp_stand_P: if tem_pre_PM < tmp_stand_PM2: # 条件都满足，则把instance放入主机 result = result.append(pd.DataFrame( [{"instanceid": df3["instanceid"][i], "machineid": "machine_" + str(j)}])) tem_disk = tem_disk + df3["disk"][i] tem_mem = tem_mem + df3["mem"][i] tem_cpu = tem_cpu + df3["cpu"][i] tem_P = tem_P + df3["P"][i] tem_M = tem_M + df3["M"][i] tem_PM = tem_PM + df3["PM"][i] df3.loc[i, "isdploy"] = True deploy_list.append(df3["instanceid"][i]) else: # 主机j没有部署实例，则先部署一个 result = result.append( pd.DataFrame([{"instanceid": df3["instanceid"][i], "machineid": "machine_" + str(j)}])) tem_disk = tem_disk + df3["disk"][i] tem_mem = tem_mem + df3["mem"][i] tem_cpu = tem_cpu + df3["cpu"][i] tem_P = tem_P + df3["P"][i] tem_M = tem_M + df3["M"][i] tem_PM = tem_PM + df3["PM"][i] df3.loc[i, "isdploy"] = True deploy_list.append(df3["instanceid"][i]) is_deploy = True def plotGroup(): # df3新建一列 df3["disk"] = None for i in range(0, 68219): df3["disk"][i] = lambda x: x[i], df1["disk"] # instance分类统计 group1 = df3.groupby("appid").count() print(type(group1)) print(group1["instanceid"].sort_values(ascending=False)) plt.plot(group1["instanceid"].sort_values(ascending=False)) plt.savefig("../submit/group1.jpg") # 找到每个instance消耗的disk # df3["disk"] = # 跑 deplay()