def non_uniform_server_heuristics(model_dist: ModelDist,
repeats: int = 20,
time_limit: int = 2,
random_repeats: int = 10,
price_change_mean: int = 4,
price_change_std: int = 2,
initial_price_mean: int = 25,
initial_price_std: int = 4):
"""
Evaluates the effect of the server heuristics when they are non-uniform (all server's dont use the same value)
:param model_dist: The model distribution
:param repeats: The number of repeats
:param time_limit: The time limit for the decentralised iterative auction
:param random_repeats: The number of random repeats for each model generated
:param price_change_mean: The mean price change value
:param price_change_std: The standard deviation of the price change value
:param initial_price_mean: The mean initial change value
:param initial_price_std: The standard deviation of the initial change value
"""
print(
f'DIA non-uniform heuristic investigation with initial price mean: {initial_price_mean} and '
f'std: {initial_price_std}, price change mean: {price_change_mean} and price change std: {price_change_std}, '
f'using {model_dist.name} model with {model_dist.num_tasks} tasks and {model_dist.num_servers} servers'
)
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('dia_non_uniform_heuristic', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers, non_elastic_tasks, algorithm_results = generate_evaluation_model(
model_dist, pretty_printer)
set_server_heuristics(servers,
price_change=price_change_mean,
initial_price=initial_price_mean)
dia_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit)
algorithm_results['normal'] = dia_result.store()
dia_result.pretty_print()
reset_model(tasks, servers)
for random_repeat in range(random_repeats):
for server in servers:
server.price_change = max(
1, int(gauss(price_change_mean, price_change_std)))
server.initial_price = max(
1, int(gauss(initial_price_mean, initial_price_std)))
dia_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit)
algorithm_results[f'repeat {random_repeat}'] = dia_result.store()
dia_result.pretty_print()
reset_model(tasks, servers)
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def dia_social_welfare_test(model_dist: ModelDist,
repeat: int,
repeats: int = 20):
"""
Evaluates the results using the optimality
:param model_dist: The model distribution
:param repeat: The repeat of the testing
:param repeats: The number of repeats
"""
data = []
filename = results_filename('testing', model_dist)
for _ in range(repeats):
tasks, servers = model_dist.generate_oneshot()
model_results = {}
optimal_result = elastic_optimal(tasks, servers, 30)
model_results[optimal_result.algorithm] = optimal_result.store()
reset_model(tasks, servers)
for pos in range(5):
set_server_heuristics(servers, price_change=3, initial_price=25)
dia_result = optimal_decentralised_iterative_auction(
tasks, servers, 2)
model_results[f'DIA {pos}'] = dia_result
reset_model(tasks, servers)
data.append(model_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(data, file)
def auction_evaluation(model_dist: ModelDist, repeats: int = 50, dia_time_limit: int = 3,
run_elastic: bool = True, run_non_elastic: bool = True):
"""
Evaluation of different auction algorithms
:param model_dist: The model distribution
:param repeats: The number of repeats
:param dia_time_limit: Decentralised iterative auction time limit
:param run_elastic: If to run the elastic vcg auction
:param run_non_elastic: If to run the non-elastic vcg auction
"""
print(f'Evaluates the auction algorithms (cva, dia, elastic vcg, non-elastic vcg) for {model_dist.name} model with '
f'{model_dist.num_tasks} tasks and {model_dist.num_servers} servers')
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('auctions', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers, non_elastic_tasks, algorithm_results = generate_evaluation_model(model_dist, pretty_printer)
if run_elastic:
# Elastic VCG Auctions
vcg_result = elastic_vcg_auction(tasks, servers, time_limit=None)
algorithm_results[vcg_result.algorithm] = vcg_result.store()
vcg_result.pretty_print()
reset_model(tasks, servers)
if run_non_elastic:
# Elastic VCG auction
vcg_result = non_elastic_vcg_auction(non_elastic_tasks, servers, time_limit=None)
algorithm_results[vcg_result.algorithm] = vcg_result.store()
vcg_result.pretty_print()
reset_model(non_elastic_tasks, servers)
# Decentralised Iterative auction
dia_result = optimal_decentralised_iterative_auction(tasks, servers, time_limit=dia_time_limit)
algorithm_results[dia_result.algorithm] = dia_result.store()
dia_result.pretty_print()
reset_model(tasks, servers)
# Critical Value Auction
for task_priority in task_priority_functions:
for server_selection_policy in server_selection_functions:
for resource_allocation_policy in resource_allocation_functions:
critical_value_result = critical_value_auction(tasks, servers, task_priority,
server_selection_policy, resource_allocation_policy)
algorithm_results[critical_value_result.algorithm] = critical_value_result.store()
critical_value_result.pretty_print()
reset_model(tasks, servers)
# Add the results to the data
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def server_resource_ratio(model_dist: ModelDist, repeats: int = 25, run_elastic: bool = True,
run_non_elastic: bool = True, non_elastic_time_limit: Optional[int] = None,
ratios: Iterable[int] = (0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9)):
"""
Evaluates the difference in social welfare when the ratio of computational to bandwidth capacity is changed between
different algorithms: greedy, elastic optimal, non-elastic optimal and server relaxed optimal
:param model_dist: The model distribution
:param repeats: The number of repeats
:param run_elastic: If to run the optimal elastic solver
:param run_non_elastic: If to run the optimal non-elastic solver
:param non_elastic_time_limit: The non-elastic optimal time limit
:param ratios: List of ratios to test
"""
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('resource_ratio', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
# Generate the tasks and servers
tasks, servers, non_elastic_tasks, ratio_results = generate_evaluation_model(model_dist, pretty_printer)
server_total_resources = {server: server.computation_capacity + server.bandwidth_capacity
for server in servers}
for ratio in ratios:
algorithm_results = {}
# Update server capacities
for server in servers:
server.update_capacities(int(server_total_resources[server] * ratio),
int(server_total_resources[server] * (1 - ratio)))
if run_elastic:
# Finds the elastic optimal solution
elastic_optimal_results = elastic_optimal(tasks, servers, time_limit=None)
algorithm_results[elastic_optimal_results.algorithm] = elastic_optimal_results.store(ratio=ratio)
pretty_printer.pprint(algorithm_results[elastic_optimal_results.algorithm])
reset_model(tasks, servers)
if run_non_elastic:
# Find the non-elastic optimal solution
non_elastic_results = non_elastic_optimal(non_elastic_tasks, servers, time_limit=non_elastic_time_limit)
algorithm_results[non_elastic_results.algorithm] = non_elastic_results.store(ratio=ratio)
non_elastic_results.pretty_print()
reset_model(non_elastic_tasks, servers)
# Loop over all of the greedy policies permutations
greedy_permutations(tasks, servers, algorithm_results)
ratio_results[f'ratio {ratio}'] = algorithm_results
model_results.append(ratio_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def dia_heuristic_grid_search(model_dist: ModelDist,
repeats: int = 50,
time_limit: int = 4,
initial_prices: Iterable[int] = (0, 4, 8, 12),
price_changes: Iterable[int] = (1, 2, 4, 6)):
"""
Evaluates the difference in results with the decentralised iterative auction uses different price changes and
initial price variables
:param model_dist: The model distribution
:param repeats: The number of repeats
:param time_limit: The time limit for the DIA Auction
:param initial_prices: The initial price for auctions
:param price_changes: The price change of the servers
"""
print(
f'DIA Heuristic grid search with initial prices: {initial_prices}, price changes: {price_changes}'
f'for {model_dist.name} model with {model_dist.num_tasks} tasks and {model_dist.num_servers} servers'
)
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('dia_heuristic_grid_search', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers, non_elastic_tasks, algorithm_results = generate_evaluation_model(
model_dist, pretty_printer)
for initial_price in initial_prices:
for price_change in price_changes:
set_server_heuristics(servers,
price_change=price_change,
initial_price=initial_price)
results = optimal_decentralised_iterative_auction(
tasks, servers, time_limit)
algorithm_results[
f'IP: {initial_price}, PC: {price_change}'] = results.store(
**{
'initial price': initial_price,
'price change': price_change
})
results.pretty_print()
reset_model(tasks, servers)
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def dia_repeat(model_dist: ModelDist,
repeats: int = 25,
auction_repeats: int = 5,
time_limit: int = 2,
price_change: int = 3,
initial_price: int = 25):
"""
Tests the Decentralised iterative auction by repeating the auction to see if the same local / global maxima is
achieved
:param model_dist: The model distribution
:param repeats: The number of repeats
:param auction_repeats: The number of auction repeats
:param time_limit: The auction time limit
:param price_change: Price change
:param initial_price: The initial price
"""
print(f'Evaluation of DIA by repeating the auction')
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('repeat_dia', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers, non_elastic_tasks, repeat_results = generate_evaluation_model(
model_dist, pretty_printer)
set_server_heuristics(servers,
price_change=price_change,
initial_price=initial_price)
for auction_repeat in range(auction_repeats):
reset_model(tasks, servers)
auction_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit=time_limit)
auction_result.pretty_print()
repeat_results[f'repeat {auction_repeat}'] = auction_result.store()
model_results.append(repeat_results)
# Save all of the results to a file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def foreknowledge_evaluation(model_dist: AlibabaModelDist, repeats: int = 50, run_elastic: bool = False):
filename = results_filename('foreknowledge', model_dist)
model_results = []
for _ in range(repeats):
servers = [model_dist.generate_server(server_id) for server_id in range(model_dist.num_servers)]
foreknowledge_tasks, requested_tasks = model_dist.generate_foreknowledge_requested_tasks(
servers, model_dist.num_tasks)
non_elastic_foreknowledge_tasks = generate_non_elastic_tasks(foreknowledge_tasks)
non_elastic_requested_tasks = generate_non_elastic_tasks(requested_tasks)
algorithm_results = {
'model': {'foreknowledge tasks': [foreknowledge_task.save() for foreknowledge_task in foreknowledge_tasks],
'requested tasks': [requested_task.save() for requested_task in requested_tasks],
'servers': [server.save() for server in servers]}}
if run_elastic:
results = elastic_optimal(foreknowledge_tasks, servers, time_limit=None)
algorithm_results['foreknowledge elastic optimal'] = results.store()
reset_model(foreknowledge_tasks, servers)
results = elastic_optimal(requested_tasks, servers, time_limit=None)
algorithm_results['requested elastic optimal'] = results.store()
reset_model(requested_tasks, servers)
results = non_elastic_optimal(non_elastic_foreknowledge_tasks, servers, time_limit=None)
algorithm_results['foreknowledge non-elastic optimal'] = results.store()
reset_model(non_elastic_foreknowledge_tasks, servers)
results = non_elastic_optimal(non_elastic_requested_tasks, servers, time_limit=None)
algorithm_results['requested non-elastic optimal'] = results.store()
reset_model(non_elastic_requested_tasks, servers)
greedy_permutations(foreknowledge_tasks, servers, algorithm_results, 'foreknowledge ')
greedy_permutations(requested_tasks, servers, algorithm_results, 'requested ')
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished')
def lower_bound_testing(model_dist: ModelDist, repeats: int = 50):
"""
Testing is to compare the lower bound of the greedy to the best greedy algorithm
:param model_dist: Model distribution
:param repeats: Repeat number
"""
print(f'Evaluates the greedy algorithm for {model_dist.name} model with '
f'{model_dist.num_tasks} tasks and {model_dist.num_servers} servers')
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('lower_bound', model_dist)
lb_task_functions = task_priority_functions + [ValuePriority()]
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers, non_elastic_tasks, algorithm_results = generate_evaluation_model(
model_dist, pretty_printer)
# Loop over all of the greedy policies permutations
for task_priority in lb_task_functions:
for server_selection in server_selection_functions:
for resource_allocation in resource_allocation_functions:
greedy_result = greedy_algorithm(tasks, servers,
task_priority,
server_selection,
resource_allocation)
algorithm_results[
greedy_result.algorithm] = greedy_result.store()
greedy_result.pretty_print()
reset_model(tasks, servers)
# Add the results to the data
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def algorithm_sizes(model_dist: ModelDist, repeats: int = 30):
"""
Runs the greedy algorithm for a range of model sizes
:param model_dist: The model distributions
:param repeats: The number of repeats for each model size
"""
print('Evaluates the greedy algorithm over a range of model sizes')
pretty_printer, scale_results = PrettyPrinter(), {}
filename = results_filename('model_sizes', model_dist)
for num_tasks, num_servers in ((10, 2), (15, 3), (20, 4), (30, 6), (40, 8),
(80, 16), (160, 32)):
print(
f'Numbers of tasks: {num_tasks}, number of servers: {num_servers}')
model_dist.num_tasks = num_tasks
model_dist.num_servers = num_servers
model_results = []
for _ in range(repeats):
tasks, servers = model_dist.generate_oneshot()
results = greedy_algorithm(
tasks,
servers,
task_priority=UtilityDeadlinePerResourcePriority(
ResourceSumPriority()),
server_selection=ProductResources(),
resource_allocation=SumPowPercentage())
model_results.append(results.store())
scale_results[
f'{num_tasks} tasks, {num_servers} servers'] = model_results
# Save the results to the file
with open(filename, 'w') as file:
json.dump(scale_results, file)
def full_task_mutation(model_dist: ModelDist,
repeats: int = 25,
time_limit: int = 2,
price_change: int = 3,
initial_price: int = 25,
model_mutations: int = 15,
mutate_percent: float = 0.15):
"""
Evaluates the effectiveness of a task mutations on if the mutated task is allocated and if so the difference in
price between the mutated and normal task
:param model_dist: Model distribution to generate tasks and servers
:param repeats: The number of model repeats
:param time_limit: The time limit for the decentralised iterative auction results
:param price_change: The price change of the servers
:param initial_price: The initial price of tasks for the servers
:param model_mutations: The number of model mutations to attempt
:param mutate_percent: The percentage of the model that it can be mutated by
"""
print(
f'Evaluates the possibility of tasks mutating resulting in a lower price'
)
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('task_mutation', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers = model_dist.generate_oneshot()
set_server_heuristics(servers,
price_change=price_change,
initial_price=initial_price)
mutation_results = {
'model': {
'tasks': [task.save() for task in tasks],
'servers': [server.save() for server in servers]
}
}
pretty_printer.pprint(mutation_results)
# Calculate the results without any mutation
no_mutation_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit=time_limit)
no_mutation_result.pretty_print()
mutation_results['no mutation'] = no_mutation_result.store()
# Save the task prices and server revenues
task_prices = {task: task.price for task in tasks}
allocated_tasks = {
task: task.running_server is not None
for task in tasks
}
to_mutate_tasks = [
task for task, allocated in allocated_tasks.items()
] # if allocated todo future testing
reset_model(tasks, servers)
# Loop each time mutating a task or server and find the auction results and compare to the unmutated result
for model_mutation in range(min(model_mutations,
len(to_mutate_tasks))):
# Choice a random task and mutate it
task: ElasticTask = to_mutate_tasks.pop(
rnd.randint(0,
len(to_mutate_tasks) - 1))
mutant_task = task.mutate(mutate_percent)
# Replace the task with the mutant task in the task list
list_item_replacement(tasks, task, mutant_task)
assert mutant_task in tasks
assert task not in tasks
# Find the result with the mutated task
mutant_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit)
mutation_results[f'mutation {model_mutation}'] = mutant_result.store(
**{
'task price': task_prices[task],
'task allocated': allocated_tasks[task],
'mutant price': mutant_task.price,
'mutant task allocated': mutant_task.running_server
is not None,
'mutant task name': task.name,
'mutant task deadline': mutant_task.deadline,
'mutant task value': mutant_task.value,
'mutant task storage': mutant_task.required_storage,
'mutant task computation':
mutant_task.required_computation,
'mutant task results data':
mutant_task.required_results_data,
})
pretty_printer.pprint(
mutation_results[f'mutation {model_mutation}'])
# Replace the mutant task with the task in the task list
list_item_replacement(tasks, mutant_task, task)
assert mutant_task not in tasks
assert task in tasks
# Append the results to the data list
model_results.append(mutation_results)
# Save all of the results to a file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def value_only_mutation(model_dist: ModelDist,
repeats: int = 25,
time_limit: int = 2,
price_change: int = 3,
initial_price: int = 25,
model_mutations: int = 15,
value_mutations: Iterable[int] = (1, 2, 3, 4)):
"""
Evaluates the value only mutation of tasks
:param model_dist: Model distribution to generate tasks and servers
:param repeats: The number of model repeats
:param time_limit: DIA time limit
:param price_change: Server price change
:param initial_price: Server initial price
:param model_mutations: The number of model mutation attempts
:param value_mutations: The value difference to do testing with
"""
print(f'Evaluates the value mutation of tasks')
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('value_mutation', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers = model_dist.generate_oneshot()
set_server_heuristics(servers,
price_change=price_change,
initial_price=initial_price)
mutation_results = {
'model': {
'tasks': [task.save() for task in tasks],
'servers': [server.save() for server in servers]
}
}
pretty_printer.pprint(mutation_results)
# Calculate the results without any mutation
no_mutation_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit=time_limit)
no_mutation_result.pretty_print()
mutation_results['no mutation'] = no_mutation_result.store()
# Save the task prices and server revenues
to_mutate_tasks = tasks[:]
reset_model(tasks, servers)
# Loop each time mutating a task or server and find the auction results and compare to the unmutated result
for model_mutation in range(min(model_mutations,
len(to_mutate_tasks))):
# Choice a random task and mutate it
task: ElasticTask = to_mutate_tasks.pop(
rnd.randint(0,
len(to_mutate_tasks) - 1))
task_value = task.value
task_mutation_results = {}
for value in value_mutations:
task.value = task_value - value
# Find the result with the mutated task
mutant_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit)
task_mutation_results[f'value {value}'] = mutant_result.store(
**{
'price': task.price,
'allocated': task.running_server is not None,
'value': task.value
})
pretty_printer.pprint(task_mutation_results[f'value {value}'])
reset_model(tasks, servers)
task.value = task_value
mutation_results[f'task {task.name}'] = task_mutation_results
# Append the results to the data list
model_results.append(mutation_results)
# Save all of the results to a file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def mutation_grid_search(model_dist: ModelDist,
percent: float = 0.10,
time_limit: int = 3,
price_change: int = 3,
initial_price: int = 30):
"""
Attempts a grid search version of the mutation testing above where a single task is mutated in every possible way
within a particular way to keep that the random testing is not missing anything
:param model_dist: The model distribution to generate servers and tasks
:param percent: The percentage by which mutations can occur within
:param time_limit: The time limit for the optimal decentralised iterative auction
:param price_change: The price change of the servers
:param initial_price: The initial price for the servers
"""
print(f'Completes a grid search of a task known to achieve better results')
filename = results_filename('mutation_grid_search', model_dist)
positive_percent, negative_percent = 1 + percent, 1 - percent
# Generate the tasks and servers
tasks, servers = model_dist.generate_oneshot()
set_server_heuristics(servers,
price_change=price_change,
initial_price=initial_price)
# The mutation results
mutation_results = {
'model': {
'tasks': [task.save() for task in tasks],
'servers': [server.save() for server in servers]
}
}
no_mutation_dia = optimal_decentralised_iterative_auction(
tasks, servers, time_limit=time_limit)
no_mutation_dia.pretty_print()
task = next(task for task in tasks if task.running_server is not None)
mutation_results['no mutation'] = no_mutation_dia.store(
**{
'allocated': task.running_server is not None,
'task price': task.price
})
# The original task not mutated that is randomly selected (given the tasks are already randomly generated)
permutations = ((int(task.required_storage * positive_percent) + 1) - task.required_storage) * \
((int(task.required_computation * positive_percent) + 1) - task.required_computation) * \
((int(task.required_results_data * positive_percent) + 1) - task.required_results_data) * \
((task.deadline + 1) - int(task.deadline * negative_percent))
print(
f'Number of permutations: {permutations}, original solve time: {no_mutation_dia.solve_time}, '
f'estimated time: {round(permutations * no_mutation_dia.solve_time / 60, 1)} minutes'
)
reset_model(tasks, servers)
mutation_pos = 0
# Loop over all of the permutations that the task requirement resources have up to the mutate percentage
for required_storage in range(
task.required_storage,
int(task.required_storage * positive_percent) + 1):
for required_computation in range(
task.required_computation,
int(task.required_computation * positive_percent) + 1):
for required_results_data in range(
task.required_results_data,
int(task.required_results_data * positive_percent) + 1):
for deadline in range(int(task.deadline * negative_percent),
task.deadline + 1):
# Create the new mutated task and create new tasks list with the mutant task replacing the task
mutant_task = ElasticTask(
f'mutated {task.name}',
required_storage=required_storage,
required_computation=required_computation,
required_results_data=required_results_data,
deadline=deadline,
value=task.value)
tasks.append(mutant_task)
# Calculate the task price with the mutated task
mutated_result = optimal_decentralised_iterative_auction(
tasks, servers, time_limit)
mutated_result.pretty_print()
mutation_results[
f'Mutation {mutation_pos}'] = mutated_result.store(
**{
'mutated task': task.name,
'task price': mutant_task.price,
'required storage': required_storage,
'required computation': required_computation,
'required results data': required_results_data,
'deadline': deadline,
'allocated': mutant_task.running_server
is not None
})
mutation_pos += 1
# Remove the mutant task and read the task to the list of tasks and reset the model
tasks.remove(mutant_task)
reset_model(tasks, servers)
# Save all of the results to a file
with open(filename, 'w') as file:
json.dump(mutation_results, file)
print('Finished running')
def online_evaluation(model_dist: ModelDist,
repeats: int = 20,
time_steps: int = 200,
mean_arrival_rate: float = 1,
std_arrival_rate: float = 2,
task_priority=UtilityDeadlinePerResourcePriority(
ResourceSumPriority()),
server_selection=ProductResources(),
resource_allocation=SumPowPercentage()):
"""
Evaluates the batch online
:param model_dist: The model distribution
:param repeats: The number of repeats
:param time_steps: Total number of time steps
:param mean_arrival_rate: Mean arrival rate of tasks
:param std_arrival_rate: Standard deviation arrival rate of tasks
:param task_priority: The task prioritisation function
:param server_selection: Server selection policy
:param resource_allocation: Resource allocation policy
"""
print(
f'Evaluates difference in performance between batch and online algorithm for {model_dist.name} model with '
f'{model_dist.num_tasks} tasks and {model_dist.num_servers} servers')
print(
f'Settings - Time steps: {time_steps}, mean arrival rate: {mean_arrival_rate} with std: {std_arrival_rate}'
)
model_results = []
filename = results_filename('online_resource_allocation', model_dist)
greedy_name = f'Greedy {task_priority.name}, {server_selection.name}, {resource_allocation.name}'
batch_length = 1
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
# Generate the tasks and servers
tasks, servers = model_dist.generate_online(time_steps,
mean_arrival_rate,
std_arrival_rate)
algorithm_results = {
'model': {
'tasks': [task.save() for task in tasks],
'servers': [server.save() for server in servers]
}
}
non_elastic_tasks = generate_non_elastic_tasks(tasks)
valid_elastic_tasks = [
task for task in tasks if batch_length < task.deadline
]
batched_elastic_tasks = generate_batch_tasks(valid_elastic_tasks,
batch_length, time_steps)
valid_non_elastic_tasks = [
task for task in non_elastic_tasks if batch_length < task.deadline
]
batched_non_elastic_tasks = generate_batch_tasks(
valid_non_elastic_tasks, batch_length, time_steps)
# Flatten the tasks
flattened_elastic_tasks = [
task for tasks in batched_elastic_tasks for task in tasks
]
flattened_non_elastic_tasks = [
task for tasks in batched_non_elastic_tasks for task in tasks
]
elastic_optimal_result = online_batch_solver(batched_elastic_tasks,
servers,
batch_length,
'Elastic Optimal',
elastic_optimal_solver,
time_limit=None)
algorithm_results[
elastic_optimal_result.algorithm] = elastic_optimal_result.store()
reset_model(flattened_elastic_tasks, servers)
non_elastic_optimal_result = online_batch_solver(
batched_non_elastic_tasks,
servers,
batch_length,
'Non-elastic Optimal',
non_elastic_optimal_solver,
time_limit=None)
algorithm_results[non_elastic_optimal_result.
algorithm] = non_elastic_optimal_result.store()
reset_model(flattened_non_elastic_tasks, servers)
# Loop over all of the greedy policies permutations
greedy_result = online_batch_solver(
batched_elastic_tasks,
servers,
batch_length,
greedy_name,
greedy_algorithm,
task_priority=task_priority,
server_selection=server_selection,
resource_allocation=resource_allocation)
algorithm_results[greedy_result.algorithm] = greedy_result.store()
reset_model(flattened_elastic_tasks, servers)
# Add the results to the data
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def greedy_permutations(model_dist: ModelDist,
repeats: int = 20,
time_steps: int = 1000,
mean_arrival_rate: float = 2,
std_arrival_rate: float = 2,
batch_length: int = 1):
"""
Evaluates the performance between greedy algorithms with different module functions
:param model_dist: The model distribution used to test with
:param repeats: The number of testing repeats that are computed
:param time_steps: The total number of time steps for tasks to arrive at
:param mean_arrival_rate: The mean arrival rate of tasks
:param std_arrival_rate: The standard deviation of the arrival rate for the task
:param batch_length: The batch length of the testing setting
"""
print(f'Evaluates performance between different greedy permutations')
print(
f'Settings - Time steps: {time_steps}, mean arrival rate: {mean_arrival_rate}, std: {std_arrival_rate}'
)
model_results = []
filename = results_filename('online_greedy_permutations', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
# Generate the tasks and servers
tasks, servers = model_dist.generate_online(time_steps,
mean_arrival_rate,
std_arrival_rate)
algorithm_results = {
'model': {
'tasks': [task.save() for task in tasks],
'servers': [server.save() for server in servers]
}
}
valid_tasks = [task for task in tasks if batch_length < task.deadline]
batched_tasks = generate_batch_tasks(valid_tasks, batch_length,
time_steps)
flattened_tasks = [task for tasks in batched_tasks for task in tasks]
for task_priority, server_selection, resource_allocation in [
(UtilityDeadlinePerResourcePriority(ResourceSumPriority()),
ProductResources(), SumPowPercentage()),
(UtilityDeadlinePerResourcePriority(ResourceSumPriority()),
ProductResources(True), SumPowPercentage()),
(UtilityDeadlinePerResourcePriority(ResourceProductPriority()),
ProductResources(), SumPowPercentage()),
(UtilityDeadlinePerResourcePriority(ResourceProductPriority()),
ProductResources(True), SumPowPercentage()),
(ValuePriority(), ProductResources(), SumPowPercentage()),
(ValuePriority(), ProductResources(), SumPowPercentage()),
(UtilityDeadlinePerResourcePriority(ResourceSumPriority()),
SumResources(), SumPowPercentage()),
(UtilityDeadlinePerResourcePriority(ResourceSumPriority()),
SumResources(True), SumPowPercentage())
]:
greedy_name = f'Greedy {task_priority.name}, {server_selection.name}, ' \
f'{resource_allocation.name}'
greedy_result = online_batch_solver(
batched_tasks,
servers,
batch_length,
greedy_name,
greedy_algorithm,
task_priority=task_priority,
server_selection=server_selection,
resource_allocation=resource_allocation)
algorithm_results[greedy_result.algorithm] = greedy_result.store()
print(greedy_name)
reset_model(flattened_tasks, servers)
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
def greedy_evaluation(model_dist: ModelDist,
repeats: int = 50,
run_elastic_optimal: bool = True,
run_non_elastic_optimal: bool = True,
run_server_relaxed_optimal: bool = True):
"""
Evaluation of different greedy algorithms
:param model_dist: The model distribution
:param repeats: Number of model runs
:param run_elastic_optimal: If to run the optimal elastic solver
:param run_non_elastic_optimal: If to run the optimal non-elastic solver
:param run_server_relaxed_optimal: If to run the relaxed elastic solver
"""
print(
f'Evaluates the greedy algorithms (plus elastic, non-elastic and server relaxed optimal solutions) '
f'for {model_dist.name} model with {model_dist.num_tasks} tasks and {model_dist.num_servers} servers'
)
pretty_printer, model_results = PrettyPrinter(), []
filename = results_filename('greedy', model_dist)
for repeat in range(repeats):
print(f'\nRepeat: {repeat}')
tasks, servers, non_elastic_tasks, algorithm_results = generate_evaluation_model(
model_dist, pretty_printer)
if run_elastic_optimal:
# Find the optimal solution
elastic_optimal_result = elastic_optimal(tasks,
servers,
time_limit=None)
algorithm_results[elastic_optimal_result.
algorithm] = elastic_optimal_result.store()
elastic_optimal_result.pretty_print()
reset_model(tasks, servers)
if run_server_relaxed_optimal:
# Find the relaxed solution
relaxed_result = server_relaxed_elastic_optimal(tasks,
servers,
time_limit=None)
algorithm_results[
relaxed_result.algorithm] = relaxed_result.store()
relaxed_result.pretty_print()
reset_model(tasks, servers)
if run_non_elastic_optimal:
# Find the non-elastic solution
non_elastic_optimal_result = non_elastic_optimal(non_elastic_tasks,
servers,
time_limit=None)
algorithm_results[non_elastic_optimal_result.
algorithm] = non_elastic_optimal_result.store()
non_elastic_optimal_result.pretty_print()
reset_model(non_elastic_tasks, servers)
# Loop over all of the greedy policies permutations
greedy_permutations(tasks, servers, algorithm_results)
# Add the results to the data
model_results.append(algorithm_results)
# Save the results to the file
with open(filename, 'w') as file:
json.dump(model_results, file)
print('Finished running')
