def run_for_n_resources(resources):
"""
Runs experiments for a number of resources.
Parameters
----------
resources : int
Number of resources
"""
print(f'- Running experiment for {resources} resources.')
# Initializes the cost matrix with zeros
cost = np.zeros(shape=(resources, max_tasks+1))
# Fills the cost matrix with costs based on a quadratic function
base_seed = rng_seed_resources
for i in range(resources):
devices.create_quadratic_costs(base_seed, cost, i, max_tasks)
base_seed += 1
# Iterates over the number of tasks running all schedulers
for tasks in range(min_tasks, max_tasks, step_tasks):
# Prepares the upper and lower limit arrays
avg_tasks = tasks // resources
lower_limit = np.full(shape=resources, fill_value=4)
upper_limit = np.full(shape=resources, fill_value=avg_tasks*2)
# Finds the resource with the maximum and minimum costs for 'tasks'
max_index = np.argmax(cost[:, tasks])
lower_limit[max_index] = avg_tasks // 4
min_index = np.argmin(cost[:, tasks])
upper_limit[min_index] = avg_tasks // 2
# 2. Run Proportional with three task values to base itself
a = schedulers.extended_proportional(tasks, resources, cost, 1,
lower_limit, upper_limit)
check_and_store(f'Proportional-(1)', tasks, resources, a, cost,
lower_limit, upper_limit)
a = schedulers.extended_proportional(tasks, resources, cost, avg_tasks,
lower_limit, upper_limit)
check_and_store(f'Proportional-(tasks/res)', tasks, resources, a, cost,
lower_limit, upper_limit)
a = schedulers.extended_proportional(tasks, resources, cost, tasks,
lower_limit, upper_limit)
check_and_store(f'Proportional-(tasks)', tasks, resources, a, cost,
lower_limit, upper_limit)
# 3. Run FedAvg
a = schedulers.extended_fedavg(tasks, resources,
lower_limit, upper_limit)
check_and_store('FedAvg', tasks, resources, a, cost,
lower_limit, upper_limit)
# 4. Run Fed-LBAP
a = schedulers.extended_fed_lbap(tasks, resources, cost,
lower_limit, upper_limit)
check_and_store('Fed-LBAP', tasks, resources, a, cost,
lower_limit, upper_limit)
# 5. Run OLAR
a = schedulers.olar(tasks, resources, cost, lower_limit, upper_limit)
check_and_store('OLAR', tasks, resources, a, cost,
lower_limit, upper_limit)
def test_extended_fed_lbap(self):
assignment = schedulers.extended_fed_lbap(self.tasks, self.resources,
self.cost, self.lower_limit,
self.upper_limit)
self.assertEqual(assignment[0], 2)
self.assertEqual(assignment[1], 1)
self.assertEqual(assignment[2], 1)
def test_extended_fed_lbap_u(self):
upper_limit = np.array([1, 3, 2])
assignment = schedulers.extended_fed_lbap(self.tasks, self.resources,
self.cost, self.lower_limit,
upper_limit)
self.assertEqual(assignment[0], 1)
self.assertEqual(assignment[1], 2)
self.assertEqual(assignment[2], 1)
def test_extended_fed_lbap_l(self):
lower_limit = np.array([0, 0, 3])
assignment = schedulers.extended_fed_lbap(self.tasks, self.resources,
self.cost, lower_limit,
self.upper_limit)
self.assertEqual(assignment[0], 0)
self.assertEqual(assignment[1], 1)
self.assertEqual(assignment[2], 3)
def test_extended_fed_lbap_l_u(self):
# corner case fixed by the verification by the end
lower_limit = np.array([0, 0, 2])
upper_limit = np.array([1, 3, 2])
assignment = schedulers.extended_fed_lbap(self.tasks, self.resources,
self.cost, lower_limit,
upper_limit)
self.assertEqual(assignment[0], 1)
self.assertEqual(assignment[1], 1)
self.assertEqual(assignment[2], 2)
def test_extended_fed_lbap_final_verification(self):
tasks = 4
resources = 3
lower_limit = np.full(shape=self.resources, fill_value=1)
upper_limit = np.full(shape=self.resources, fill_value=3)
cost = np.array([[0.0, 1.0, 2.0, 3.0, 4.0], [0.0, 1.0, 2.0, 3.0, 4.2],
[0.0, 1.0, 2.0, 3.0, 4.5]])
assignment = schedulers.extended_fed_lbap(tasks, resources, cost,
lower_limit, upper_limit)
self.assertEqual(assignment[0], 1)
self.assertEqual(assignment[1], 1)
self.assertEqual(assignment[2], 2)