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 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 mixed function
base_seed = rng_seed_resources
for i in range(resources):
if i % 4 == 0:
devices.create_recursive_costs(base_seed, cost, i, max_tasks)
elif i % 4 == 1:
devices.create_linear_costs(base_seed, cost, i, max_tasks)
elif i % 4 == 2:
devices.create_nlogn_costs(base_seed, cost, i, max_tasks)
else:
devices.create_quadratic_costs(base_seed, cost, i, max_tasks)
base_seed += 1
# Prepares the upper and lower limit arrays
lower_limit = np.zeros(shape=resources, dtype=int)
upper_limit = np.full(shape=resources, fill_value=max_tasks + 1, dtype=int)
# Iterates over the number of tasks running all schedulers
iteration = 0
for tasks in range(min_tasks, max_tasks, step_tasks):
# 1. Run Random with three seeds
for seed in seeds_for_random:
a = schedulers.random(tasks, resources, seed + iteration)
check_and_store(f'Random-(seed:{seed})', tasks, resources, a, cost)
iteration += 1
# 2. Run Proportional with three task values to base itself
a = schedulers.proportional(tasks, resources, cost, 1)
check_and_store(f'Proportional-(1)', tasks, resources, a, cost)
a = schedulers.proportional(tasks, resources, cost, tasks // resources)
check_and_store(f'Proportional-(tasks/res)', tasks, resources, a, cost)
a = schedulers.proportional(tasks, resources, cost, tasks)
check_and_store(f'Proportional-(tasks)', tasks, resources, a, cost)
# 3. Run FedAvg
a = schedulers.fedavg(tasks, resources)
check_and_store('FedAvg', tasks, resources, a, cost)
# 4. Run Fed-LBAP
a = schedulers.fed_lbap(tasks, resources, cost)
check_and_store('Fed-LBAP', tasks, resources, a, cost)
# 5. Run OLAR
a = schedulers.olar(tasks, resources, cost, lower_limit, upper_limit)
check_and_store('OLAR', tasks, resources, a, cost)
def test_quadratic(self):
devices.create_quadratic_costs(20, self.matrix, 0, self.size)
self.assertEqual(self.matrix[0][0], 6.293177209695468)
self.assertEqual(self.matrix[0][1], 24.396377326152603)
self.assertEqual(self.matrix[0][2], 60.54713057315448)
self.assertEqual(self.matrix[0][3], 114.7454369507011)
devices.create_quadratic_costs(30, self.matrix, 1, self.size)
self.assertEqual(self.matrix[1][0], 6.797291824615015)
self.assertEqual(self.matrix[1][1], 18.191459379352082)
self.assertEqual(self.matrix[1][2], 43.52048923013118)
self.assertEqual(self.matrix[1][3], 82.78438137695233)