samy.mputu
/
RTSA-lab02-SchedTest
forked from nils.hoelscher/RTSA-lab02-SchedTest
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

state before helpDesk

This commit is contained in:
Mukendi Mputu 2022-06-30 14:06:36 +02:00
parent 49c1a64e52
commit 317b8a1a9c
Signed by: samy.mputu
GPG Key ID: 492A6E5AC70F6B0B
1 changed files with 24 additions and 15 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

39
schedTests/TimeDemandAnalysis.py
View File

@ -14,15 +14,18 @@ import include.TasksHelper as TH
# The number of tasks can be accessed as: tasks.shape[0] # The number of tasks can be accessed as: tasks.shape[0]
# The Time Demand Analysis Test # The Time Demand Analysis Test
set_num = 0
def test(tasks): def test(tasks):
# Sorting Taskset by Period/Deadline # Sorting Taskset by Period/Deadline
# This makes implementing TDA a lot easier # This makes implementing TDA a lot easier
shape = tasks.shape shape = tasks.shape
sortedtasks = tasks[tasks[:, 0].argsort()] sortedtasks = tasks[tasks[:, 0].argsort()]
isSchedulable = True
global set_num
set_num += 1
print("\n======= TASK SET =======") print(f"\n======= TASK SET #{set_num} =======\n")
# For each tasks in the ordered set # For each tasks in the ordered set
for i in range(len(sortedtasks)): for i in range(len(sortedtasks)):
@ -30,25 +33,31 @@ def test(tasks):
# calculate the time points for the demand function # 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) # 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) # list_of_t = [
for j in range(1, int(np.ceil(TH.D_i(sortedtasks, i) / TH.P_i(sortedtasks, k))))] # j * TH.P_i(sortedtasks, k)
# for k in range(i-1)
# for j in range(1, int(math.ceil(TH.D_i(sortedtasks, i) / TH.P_i(sortedtasks, k))))
# ]
# list_of_t = [TH.P_i(sortedtasks, k-1) for k in range(i+1)]
list_of_t = []
for k in range(i):
list_of_t.append(TH.P_i(sortedtasks, k-1))
# print(f'\t list of ts: {list_of_t}') print(f'\t list of t: {list_of_t}')
# at any time t between 0 and and TH.P_i # at any time t between 0 and and TH.P_i
for t in np.sort(list_of_t): for j in range(len(list_of_t)):
# for t in range(TH.C_i(sortedtasks, i), TH.P_i(sortedtasks, i)+1, TH.P_i(sortedtasks, i)): # for t in range(int(TH.C_i(sortedtasks, i)), int(TH.P_i(sortedtasks, i)+1), int(step)):
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 the demand for CPU time of task i exceeds the available time t
if time_demand_func(sortedtasks, i, t) > t: if time_demand_func(sortedtasks, i, list_of_t[j]) > list_of_t[j]:
return False # then the task i will not meet its deadline, hence taskset not schedulable isSchedulable = False # then the task i will not meet its deadline, hence taskset not schedulable
return True print(f'\t time-demand for t := {list_of_t[j]} ---> {time_demand_func(sortedtasks, i, list_of_t[j])} is schedulable: {isSchedulable}')
return isSchedulable
def time_demand_func(tasks, i, delta=0): def time_demand_func(tasks, i, t):
sum = 0 sum = 0
for k in range(1, i-1): for k in range(i-1):
sum += math.ceil(delta / TH.P_i(tasks, k)) * TH.C_i(tasks, k) sum += math.ceil(t / TH.P_i(tasks, k)) * TH.C_i(tasks, k)
return TH.C_i(tasks, i) + sum return TH.C_i(tasks, i) + sum