started with TDA

This commit is contained in:
Mukendi Mputu 2022-06-27 00:22:45 +02:00
parent e398f3a492
commit 49c1a64e52
Signed by: samy.mputu
GPG Key ID: 492A6E5AC70F6B0B
2 changed files with 37 additions and 10 deletions

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@ -19,9 +19,9 @@ def test(tasks):
U_lub = n * ((2 ** (1 / n)) - 1)
# for fewer tasks than 10, we use the exact computed least upper bound
if n < 10:
return U <= U_lub
# if n < 10:
# return U <= U_lub
# from 10 tasks up unlimited, we use the limes of n(2 ** 1/n - 1)
return U <= np.log(2) # round to 0.7 ?
return U <= U_lub # np.log(2)

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@ -13,15 +13,42 @@ import include.TasksHelper as TH
# C_i is accessed as: tasks[i][2]
# The number of tasks can be accessed as: tasks.shape[0]
#The Time Demand Analysis Test
# The Time Demand Analysis Test
def test(tasks):
#Sorting Taskset by Period/Deadline
#This makes implementing TDA a lot easier
# Sorting Taskset by Period/Deadline
# This makes implementing TDA a lot easier
shape = tasks.shape
sortedtasks = tasks[tasks[:, 0].argsort()]
#####################
#YOUR CODE GOES HERE#
#####################
print("\n======= TASK SET =======")
# For each tasks in the ordered set
for i in range(len(sortedtasks)):
return False
print(f'Task #{i} {tasks[i]}:')
# calculate the time points for the demand function
# t = j * P_k for k = 1, 2,...i and j = 1, 2,...,math.ceil(P_i / P_k)
list_of_t = [TH.P_i(sortedtasks, k) * j for k in range(1, i)
for j in range(1, int(np.ceil(TH.D_i(sortedtasks, i) / TH.P_i(sortedtasks, k))))]
# print(f'\t list of ts: {list_of_t}')
# at any time t between 0 and and TH.P_i
for t in np.sort(list_of_t):
# for t in range(TH.C_i(sortedtasks, i), TH.P_i(sortedtasks, i)+1, TH.P_i(sortedtasks, i)):
print(f'\tTime-Demand for t ({t}): {time_demand_func(sortedtasks, i, t)} ')
# if the demand for CPU time of task i exceeds the available time t
if time_demand_func(sortedtasks, i, t) > t:
return False # then the task i will not meet its deadline, hence taskset not schedulable
return True
def time_demand_func(tasks, i, delta=0):
sum = 0
for k in range(1, i-1):
sum += math.ceil(delta / TH.P_i(tasks, k)) * TH.C_i(tasks, k)
return TH.C_i(tasks, i) + sum