python实现的EDF(earliest deadline first)算法

1. 最早截止时间优先EDF（Earliest DeadlineFirst）算法是非常著名的实时调度算法之一。在每一个新的就绪状态，调度器都是从那些已就绪但还没有完全处理完毕的任务中选择最早截止时间的任务，并将执行该任务所需的资源分配给它。在有新任务到来时，调度器必须立即计算EDF，排出新的定序，即正在运行的任务被剥夺，并且按照新任务的截止时间决定是否调度该新任务。如果新任务的最后期限早于被中断的当前任务，就立即处理新任务。按照EDF算法，被中断任务的处理将在稍后继续进行。

2. 该算法的思想是从两个任务中选择截至时间最早的任务,把它暂作为当前处理任务,再判断该任务是否在当前周期内,若不在当前周期内,就让另一任务暂作当前处理任务,若该任务也不在当前周期内,就让CPU空跑到最靠近的下一个截至时间的开始,若有任务在该周期内,就判断该任务的剩余时间是否小于当前截至时间与当前时间的差,若小于,则让该任务运行到结束.否则,就让该任务运行到该周期的截止时间,就立即抢回处理器,再判断紧接着的最早截至时间,并把处理器给它,做法同上,如此反复执行.

  1 #!/user/bin/env python
  2 # -*- coding:utf-8 -*-
  3
  4
  5 class GetCloseTime:
  6     def __init__(self, dictionary):
  7         self.dictionary = dictionary
  8
  9     def greatest_common_divisor(self, _left, _right):
 10         return _left if _right == 0 else self.greatest_common_divisor(_right, _left % _right)
 11
 12     def lowest_common_multiple(self):
 13         temp_result = 1
 14         for value in self.dictionary.values():
 15             temp_result = value[1] * temp_result / self.greatest_common_divisor(value[1], temp_result)
 16         return temp_result
 17
 18
 19 class TaskControlBlock:
 20     CURRENT_TIME = 0
 21
 22     def __init__(self, dictionary,
 23                  name_list,
 24                  period_time,
 25                  central_processing_unit_time,
 26                  remain_time,
 27                  current_period):
 28         for key in dictionary.keys():
 29             name_list.append(key)
 30             period_time.append(dictionary.get(key)[1])
 31             central_processing_unit_time.append(dictionary.get(key)[0])
 32             remain_time.append(dictionary.get(key)[0])
 33             current_period.append(1)
 34
 35     def get_index_of_min(self, earliest_deadline_task_list, minimum):
 36         return earliest_deadline_task_list.index(minimum)
 37
 38     def get_another_index_of_min(self, earliest_deadline_task_list, minimum):
 39         earliest_deadline_task_list[earliest_deadline_task_list.index(minimum)] = 100000
 40         return earliest_deadline_task_list.index(min(earliest_deadline_task_list))
 41
 42     def is_execute(self, central_processing_unit_time, period_time):
 43         temp_list = [a / b for a, b in zip(central_processing_unit_time, period_time)]
 44         return sum(temp_list)
 45
 46     def scheduling(self, name_list,
 47                    period_time,
 48                    central_processing_unit_time,
 49                    remain_time,
 50                    current_period):
 51         if self.is_execute(central_processing_unit_time, period_time) > 1:
 52             print("error, scheduling finish!")
 53             exit(0)
 54         earliest_deadline_task = self.get_index_of_min(
 55             [a * b for a, b in zip(period_time, current_period)],
 56             min(a * b for a, b in zip(period_time, current_period)))
 57
 58         if self.CURRENT_TIME < period_time[earliest_deadline_task] * (current_period[earliest_deadline_task] - 1):
 59             current_period_p = period_time[earliest_deadline_task] * (current_period[earliest_deadline_task] - 1)
 60             temp_list = [a * b for a, b in zip(period_time, current_period)]
 61             while self.CURRENT_TIME < period_time[earliest_deadline_task] * \
 62                     (current_period[earliest_deadline_task] - 1):
 63                 earliest_deadline_task = self.get_another_index_of_min(temp_list, min(temp_list))
 64
 65             if remain_time[earliest_deadline_task] <= current_period_p - self.CURRENT_TIME:
 66                 running_time = remain_time[earliest_deadline_task]
 67             else:
 68                 running_time = current_period_p - self.CURRENT_TIME
 69 #            current_period_p = period_time[earliest_deadline_task] * current_period[earliest_deadline_task]
 70             remain_time[earliest_deadline_task] -= running_time
 71             print(name_list[earliest_deadline_task], self.CURRENT_TIME, running_time)
 72             self.CURRENT_TIME += running_time
 73             if remain_time[earliest_deadline_task] == 0:
 74                 current_period[earliest_deadline_task] += 1
 75                 remain_time[earliest_deadline_task] = central_processing_unit_time[earliest_deadline_task]
 76         else:
 77             current_period_p = period_time[earliest_deadline_task] * current_period[earliest_deadline_task]
 78             if remain_time[earliest_deadline_task] <= current_period_p - self.CURRENT_TIME:
 79                 running_time = remain_time[earliest_deadline_task]
 80             else:
 81                 running_time = current_period_p - self.CURRENT_TIME
 82             remain_time[earliest_deadline_task] -= running_time
 83             print(name_list[earliest_deadline_task], self.CURRENT_TIME, running_time)
 84             self.CURRENT_TIME += running_time
 85             if remain_time[earliest_deadline_task] == 0:
 86                 current_period[earliest_deadline_task] += 1
 87                 remain_time[earliest_deadline_task] = central_processing_unit_time[earliest_deadline_task]
 88
 89
 90 if __name__ == "__main__":
 91     task_dictionary = {"A": [10, 30],
 92                        "B": [20, 60],
 93                        "C": [30, 90]}
 94     close_time_object = GetCloseTime(task_dictionary)
 95     close_time = close_time_object.lowest_common_multiple()
 96
 97     current_time = 0
 98     name_list = []
 99     period_time = []
100     central_processing_unit_time = []
101     remain_time = []
102     current_period = []
103     tcb = TaskControlBlock(task_dictionary,
104                            name_list,
105                            period_time,
106                            central_processing_unit_time,
107                            remain_time,
108                            current_period)
109
110     while tcb.CURRENT_TIME < close_time:
111         tcb.scheduling(name_list,
112                        period_time,
113                        central_processing_unit_time,
114                        remain_time,
115                        current_period)

EDF算法

转载于:https://www.cnblogs.com/gebicungaha/p/9994214.html

python实现的EDF(earliest deadline first)算法相关推荐

使用Python，OpenCV的Meanshift 和 Camshift 算法来查找和跟踪视频中的对象
使用Python,OpenCV的Meanshift 和 Camshift 算法来查找和跟踪视频中的对象 1. 效果图 2. 源码 2.1 MeanShift 2.2 Camshift(Continuo ...
八十五、Python | Leetcode数据结构之图和动态规划算法系列
@Author:Runsen @Date:2020/7/7 人生最重要的不是所站的位置,而是内心所朝的方向.只要我在每篇博文中写得自己体会,修炼身心:在每天的不断重复学习中,耐住寂寞,练就真功,不畏艰 ...
Python+OpenCV：理解k近邻(kNN)算法（k-Nearest Neighbour (kNN) algorithm）
Python+OpenCV:理解k近邻(kNN)算法(k-Nearest Neighbour (kNN) algorithm) 理论 kNN is one of the simplest classi ...
python:实现balanced parentheses平衡括号表达式算法(附完整源码)
python:实现balanced parentheses平衡括号表达式算法 from .stack import Stack def balanced_parentheses(parentheses ...
Python:实现gauss easte高斯复活节日期算法(附完整源码)
Python:实现gauss easte高斯复活节日期算法 import math from datetime import datetime, timedeltadef gauss_easter(y ...
Python数据挖掘入门与实践-OneR分类算法
Python数据挖掘入门与实践-OneR分类算法 OneR算法 OneR算法是根据已有的数据中,具有相同特征值的个体最可能属于哪个类别进行分类. 在本例中,只需选区Iris是个特征中分类效果最好的一个 ...
Python:实现first come first served先到先得算法(附完整源码)
Python:实现first come first served先到先得算法 from __future__ import annotationsdef calculate_waiting_times ...
python:实现9×9二维数组数独算法(附完整源码)
python:实现9×9二维数组数独算法 from __future__ import annotationsMatrix = list[list[int]]# assigning initial v ...
掌握python机器学习-读书笔记8 (抽样回归算法)
为什么80%的码农都做不了架构师?>>> 12 抽查回归算法今天来学习如何使用python scikit learn 来进行一些回归算法的使用. 篇幅限制, 不会对具体算法做 ...
python实现kd树以及最近邻查找算法
python实现kd树以及最近邻查找算法一.kd树简介二.kd树生成 1.确定切分域 2.确定数据域 3.理解递归树 4.python实现递归树代码三.kd树上的最近邻查找算法 1.生成搜索路径 ...

python实现的EDF(earliest deadline first)算法

python实现的EDF(earliest deadline first)算法相关推荐

最新文章

热门文章