def init_resource_config(redis_db, default_mr_config, machine_type):
print 'Initializing the Resource Configurations in the containers'
instance_specs = get_instance_specs(machine_type)
for mr in default_mr_config:
new_resource_provision = default_mr_config[mr]
if check_improve_mr_viability(redis_db, mr,
new_resource_provision) is False:
print 'Initial Resource provisioning for {} is too much. Exiting...'.format(
mr.to_string())
exit()
# Enact the change in resource provisioning
resource_modifier.set_mr_provision(mr, new_resource_provision)
# Reflect the change in Redis
resource_datastore.write_mr_alloc(redis_db, mr, new_resource_provision)
update_machine_consumption(redis_db, mr, new_resource_provision, 0)
def apply_pipeline_filter(redis_db,
mr_working_set,
experiment_iteration,
system_config,
workload_config,
filter_config):
print '*' * 20
print 'INFO: Applying Filtering Pipeline'
print 'Filter config is {}'.format(filter_config)
print 'MR working set is {}'.format(mr_working_set)
machine_type = system_config['machine_type']
pipeline_partitions = filter_config['pipeline_partitions']
stress_weight = filter_config['stress_amount']
experiment_trials = filter_config['filter_exp_trials']
pipelined_services = filter_config['pipeline_services']
pipeline_groups = []
print 'Pipelined services are {}'.format(pipelined_services)
# No specified pipelined services indicates that each pipeline is a service
if pipelined_services[0][0] == 'BY_SERVICE':
service_names = list(set([mr.service_name for mr in mr_working_set]))
for service_name in service_names:
mr_list = search_mr_working_set(mr_working_set, services)
pipeline_groups.append(mr_list)
elif pipelined_services[0][0] == 'RANDOM':
pipeline_groups = gen_mr_random_split(mr_working_set, pipeline_partitions)
print "The pipeline groups are being printed below: "
for pipeline_group in pipeline_groups:
pipeline_group = [mr.to_string() for mr in pipeline_group]
print 'A pipeline is {}'.format(pipeline_group)
tbot_metric = workload_config['tbot_metric']
optimize_for_lowest = workload_config['optimize_for_lowest']
pipeline_index = 0
for pipeline in pipeline_groups:
change_mr_schedule = calculate_mr_gradient_schedule(redis_db,
pipeline,
system_config,
stress_weight)
# Simultaneously stress the MRs in a pipeline
for mr in change_mr_schedule:
resource_modifier.set_mr_provision(mr, change_mr_schedule[mr], workload_config)
experiment_results = measure_runtime(workload_config, experiment_trials)
exp_mean = mean_list(experiment_results[tbot_metric])
repr_str = str(pipeline_index)
tbot_datastore.write_filtered_results(redis_db,
'pipeline',
experiment_iteration,
repr_str,
exp_mean)
# Revert the stressing
change_mr_schedule = revert_mr_gradient_schedule(redis_db,
pipeline,
system_config,
stress_weight)
for mr in change_mr_schedule:
resource_modifier.set_mr_provision(mr, change_mr_schedule[mr], workload_config)
pipeline_index += 1
all_pipeline_score_list = tbot_datastore.get_top_n_filtered_results(redis_db,
'pipeline',
experiment_iteration,
system_config,
optimize_for_lowest=optimize_for_lowest)
print 'INFO: The current pipeline score list is here {}'.format(all_pipeline_score_list)
# Temporarily just choose the most impacted pipeline
selected_pipeline_score_list = [all_pipeline_score_list[0]]
# MIP = Most Impacted Pipeline
mip = []
for pipeline_score in selected_pipeline_score_list:
pipeline_repr,score = pipeline_score
mip += pipeline_groups[int(pipeline_repr)]
print mip
# Log results of the filtering
print 'About to log to {}'.format(FILTER_LOGS)
with open(FILTER_LOGS, "a") as myfile:
# First output the result
filter_str = '{},'.format(experiment_iteration)
for mr in mip:
filter_str += '{},'.format(mr.to_string())
filter_str += '\n\n'
myfile.write(filter_str)
return mip
def run(sys_config,
workload_config,
filter_config,
default_mr_config,
last_completed_iter=0):
redis_host = sys_config['redis_host']
baseline_trials = sys_config['baseline_trials']
experiment_trials = sys_config['trials']
stress_weights = sys_config['stress_weights']
stress_policy = sys_config['stress_policy']
resource_to_stress = sys_config['stress_these_resources']
service_to_stress = sys_config['stress_these_services']
vm_to_stress = sys_config['stress_these_machines']
machine_type = sys_config['machine_type']
quilt_overhead = sys_config['quilt_overhead']
gradient_mode = sys_config['gradient_mode']
preferred_performance_metric = workload_config['tbot_metric']
optimize_for_lowest = workload_config['optimize_for_lowest']
redis_db = redis.StrictRedis(host=redis_host, port=6379, db=0)
if last_completed_iter == 0:
redis_db.flushall()
'''
# Prompt the user to make sure they want to flush the db
ok_to_flush = raw_input("Are you sure you want to flush the results of your last experiment? Please respond with Y or N: ")
if ok_to_flush == 'Y':
redis_db.flushall()
elif ok_to_flush == 'N':
print 'OK you said it boss. Exiting...'
exit()
else:
print 'Only Y and N are acceptable responses. Exiting...'
exit()
'''
print '\n' * 2
print '*' * 20
print 'INFO: INITIALIZING RESOURCE CONFIG'
# Initialize Redis and Cluster based on the default resource configuration
init_cluster_capacities_r(redis_db, machine_type, quilt_overhead)
init_service_placement_r(redis_db, default_mr_config)
init_resource_config(redis_db, default_mr_config, machine_type)
print '*' * 20
print 'INFO: INSTALLING DEPENDENCIES'
#install_dependencies(workload_config)
# Initialize time for data charts
time_start = datetime.datetime.now()
print '*' * 20
print 'INFO: RUNNING BASELINE'
# Get the Current Performance -- not used for any analysis, just to benchmark progress!!
current_performance = measure_baseline(workload_config, baseline_trials)
current_performance[preferred_performance_metric] = remove_outlier(
current_performance[preferred_performance_metric])
current_time_stop = datetime.datetime.now()
time_delta = current_time_stop - time_start
print 'Current (non-analytic) performance measured: {}'.format(
current_performance)
if last_completed_iter != 0:
tbot_datastore.write_summary_redis(
redis_db, 0, MR('initial', 'initial', []), 0, {},
mean_list(current_performance[preferred_performance_metric]),
mean_list(current_performance[preferred_performance_metric]),
time_delta.seconds, 0)
print '============================================'
print '\n' * 2
# Initialize the current configurations
# Initialize the working set of MRs to all the MRs
mr_working_set = resource_datastore.get_all_mrs(redis_db)
resource_datastore.write_mr_working_set(redis_db, mr_working_set, 0)
cumulative_mr_count = 0
experiment_count = last_completed_iter + 1
while experiment_count < 10:
# Calculate the analytic baseline that is used to determine MRs
analytic_provisions = prepare_analytic_baseline(
redis_db, sys_config, min(stress_weights))
print 'The Analytic provisions are as follows {}'.format(
analytic_provisions)
for mr in analytic_provisions:
resource_modifier.set_mr_provision(mr, analytic_provisions[mr])
analytic_baseline = measure_runtime(workload_config, experiment_trials)
analytic_mean = mean_list(
analytic_baseline[preferred_performance_metric])
print 'The analytic baseline is {}'.format(analytic_baseline)
print 'This current performance is {}'.format(current_performance)
analytic_baseline[preferred_performance_metric] = remove_outlier(
analytic_baseline[preferred_performance_metric])
# Get a list of MRs to stress in the form of a list of MRs
mr_to_consider = apply_filtering_policy(redis_db, mr_working_set,
experiment_count, sys_config,
workload_config, filter_config)
for mr in mr_to_consider:
print '\n' * 2
print '*' * 20
print 'Current MR is {}'.format(mr.to_string())
increment_to_performance = {}
current_mr_allocation = resource_datastore.read_mr_alloc(
redis_db, mr)
print 'Current MR allocation is {}'.format(current_mr_allocation)
for stress_weight in stress_weights:
# Calculate Gradient Schedule and provision resources accordingly
mr_gradient_schedule = calculate_mr_gradient_schedule(
redis_db, [mr], sys_config, stress_weight)
for change_mr in mr_gradient_schedule:
resource_modifier.set_mr_provision(
change_mr, mr_gradient_schedule[change_mr])
experiment_results = measure_runtime(workload_config,
experiment_trials)
# Write results of experiment to Redis
# preferred_results = remove_outlier(experiment_results[preferred_performance_metric])
preferred_results = experiment_results[
preferred_performance_metric]
mean_result = mean_list(preferred_results)
tbot_datastore.write_redis_ranking(
redis_db, experiment_count, preferred_performance_metric,
mean_result, mr, stress_weight)
# Revert the Gradient schedule and provision resources accordingly
mr_revert_gradient_schedule = revert_mr_gradient_schedule(
redis_db, [mr], sys_config, stress_weight)
for change_mr in mr_revert_gradient_schedule:
resource_modifier.set_mr_provision(
change_mr, mr_revert_gradient_schedule[change_mr])
increment_to_performance[stress_weight] = experiment_results
# Write the results of the iteration to Redis
tbot_datastore.write_redis_results(redis_db, mr,
increment_to_performance,
experiment_count,
preferred_performance_metric)
print '*' * 20
print '\n' * 2
# Timing Information for the purpose of experiments
current_time_stop = datetime.datetime.now()
time_delta = current_time_stop - time_start
cumulative_mr_count += len(mr_to_consider)
chart_generator.get_summary_mimr_charts(
redis_db, workload_config, current_performance, mr_working_set,
experiment_count, stress_weights, preferred_performance_metric,
time_start)
# Move back into the normal operating basis by removing the baseline prep stresses
reverted_analytic_provisions = revert_analytic_baseline(
redis_db, sys_config)
for mr in reverted_analytic_provisions:
resource_modifier.set_mr_provision(
mr, reverted_analytic_provisions[mr])
# Recover the results of the experiment from Redis
max_stress_weight = min(stress_weights)
mimr_list = tbot_datastore.get_top_n_mimr(
redis_db,
experiment_count,
preferred_performance_metric,
max_stress_weight,
gradient_mode,
optimize_for_lowest=optimize_for_lowest,
num_results_returned=-1)
imr_list, nimr_list = seperate_mr(
mimr_list,
mean_list(analytic_baseline[preferred_performance_metric]),
optimize_for_lowest)
if len(imr_list) == 0:
print 'INFO: IMR list length is 0. Please choose a metric with more signal. Exiting...'
break
print 'INFO: IMR list is {}'.format(
[mr.to_string() for mr in imr_list])
print 'INFO: NIMR list is {}'.format(
[mr.to_string() for mr in nimr_list])
# Try all the MIMRs in the list until a viable improvement is determined
# Improvement Amount
mimr = None
action_taken = {}
for imr in imr_list:
imr_improvement_percent = improve_mr_by(redis_db, imr,
max_stress_weight)
current_imr_alloc = resource_datastore.read_mr_alloc(redis_db, imr)
new_imr_alloc = convert_percent_to_raw(imr, current_imr_alloc,
imr_improvement_percent)
imr_improvement_proposal = new_imr_alloc - current_imr_alloc
# If the the Proposed MR cannot be improved by the proposed amount, there are two options
# - Max out the resources to fill up the remaining resources on the machine
# - Resource Stealing from NIMRs
# Both functions will return VIABLE improvements to the IMR deployment
nimr_diff_proposal = {}
if check_improve_mr_viability(redis_db, imr,
imr_improvement_proposal) is False:
print 'INFO: MR {} to increase {} by {} is not viable'.format(
imr.to_string(), current_imr_alloc,
imr_improvement_proposal)
print 'INFO: Attempting to max out the machines resources...'
imr_improvement_proposal = fill_out_resource(redis_db, imr)
if imr_improvement_proposal <= 0:
print 'INFO: No more space to fill out resources. Stealing from NIMRs'
# Calculate a plan to reduce the resource provisioning of NIMRs
nimr_diff_proposal, imr_improvement_proposal = create_decrease_nimr_schedule(
redis_db, imr, nimr_list, max_stress_weight)
print 'INFO: Proposed NIMR {}'.format(nimr_diff_proposal)
print 'INFO: New IMR improvement {}'.format(
imr_improvement_proposal)
if len(nimr_diff_proposal
) == 0 or imr_improvement_proposal == 0:
action_taken[imr] = 0
continue
# Decrease the amount of resources provisioned to the NIMR
for nimr in nimr_diff_proposal:
action_taken[nimr] = nimr_diff_proposal[nimr]
new_nimr_alloc = resource_datastore.read_mr_alloc(
redis_db, nimr) + nimr_diff_proposal[nimr]
print 'NIMR stealing: imposing a change of {} on {}'.format(
action_taken[nimr], nimr.to_string())
finalize_mr_provision(redis_db, nimr, new_nimr_alloc)
# Improving the resource should always be viable at this step
if check_improve_mr_viability(redis_db, imr,
imr_improvement_proposal):
new_imr_alloc = imr_improvement_proposal + current_imr_alloc
action_taken[imr] = imr_improvement_proposal
finalize_mr_provision(redis_db, imr, new_imr_alloc)
print 'Improvement Calculated: MR {} increase from {} to {}'.format(
mr.to_string(), current_imr_alloc, new_imr_alloc)
mimr = imr
break
else:
action_taken[imr] = 0
print 'Improvement Calculated: MR {} failed to improve from {}'.format(
mr.to_string(), current_mr_allocation)
print 'This IMR cannot be improved. Printing some debugging before exiting...'
print 'Current MR allocation is {}'.format(current_imr_alloc)
print 'Proposed (failed) allocation is {}, improved by {}'.format(
new_imr_alloc, imr_improvement_proposal)
for deployment in imr.instances:
vm_ip, container = deployment
capacity = resource_datastore.read_machine_capacity(
redis_db, vm_ip)
consumption = resource_datastore.read_machine_consumption(
redis_db, vm_ip)
print 'Machine {} Capacity is {}, and consumption is currently {}'.format(
vm_ip, capacity, consumption)
if mimr is None:
print 'No viable improvement found'
break
#Compare against the baseline at the beginning of the program
improved_performance = measure_runtime(workload_config,
baseline_trials)
# improved_performance[preferred_performance_metric] = remove_outlier(improved_performance[preferred_performance_metric])
improved_mean = mean_list(
improved_performance[preferred_performance_metric])
previous_mean = mean_list(
current_performance[preferred_performance_metric])
performance_improvement = improved_mean - previous_mean
# Write a summary of the experiment's iterations to Redis
tbot_datastore.write_summary_redis(redis_db, experiment_count, mimr,
performance_improvement,
action_taken, analytic_mean,
improved_mean, time_delta.seconds,
cumulative_mr_count)
current_performance = improved_performance
# Generating overall performance improvement
chart_generator.get_summary_performance_charts(redis_db,
workload_config,
experiment_count,
time_start)
results = tbot_datastore.read_summary_redis(redis_db, experiment_count)
print 'Results from iteration {} are {}'.format(
experiment_count, results)
# Checkpoint MR configurations and print
current_mr_config = resource_datastore.read_all_mr_alloc(redis_db)
print_csv_configuration(current_mr_config)
experiment_count += 1
print '{} experiments completed'.format(experiment_count)
print_all_steps(redis_db, experiment_count)
current_mr_config = resource_datastore.read_all_mr_alloc(redis_db)
for mr in current_mr_config:
print '{} = {}'.format(mr.to_string(), current_mr_config[mr])
print_csv_configuration(current_mr_config)
def finalize_mr_provision(redis_db, mr, new_alloc):
resource_modifier.set_mr_provision(mr, new_alloc)
old_alloc = resource_datastore.read_mr_alloc(redis_db, mr)
resource_datastore.write_mr_alloc(redis_db, mr, new_alloc)
update_machine_consumption(redis_db, mr, new_alloc, old_alloc)
def find_colocated_nimrs(redis_db, imr, mr_working_set, baseline_mean,
sys_config, workload_config):
print 'Finding colocated NIMRs'
experiment_trials = sys_config['trials']
stress_weights = sys_config['stress_weights']
stress_weight = min(stress_weights)
preferred_performance_metric = workload_config['tbot_metric']
optimize_for_lowest = workload_config['optimize_for_lowest']
vm_to_service = get_vm_to_service(get_actual_vms())
colocated_services = []
# Identify an unique list of relevant MRs colocated with IMR instances
for deployment in imr.instances:
vm_ip, container = deployment
colocated_services = colocated_services + vm_to_service[vm_ip]
print 'Colocated services are {}'.format(colocated_services)
candidate_mrs = []
for mr in mr_working_set:
if mr.service_name in colocated_services and mr.resource == imr.resource:
candidate_mrs.append(mr)
print 'Candidate MRs are {}'.format(
[mr.to_string() for mr in candidate_mrs])
nimr_list = []
for mr in candidate_mrs:
print 'MR being considered is {}'.format(mr.to_string())
mr_gradient_schedule = calculate_mr_gradient_schedule(
redis_db, [mr], sys_config, stress_weight)
for change_mr in mr_gradient_schedule:
resource_modifier.set_mr_provision(change_mr,
mr_gradient_schedule[change_mr],
workload_config)
experiment_results = measure_runtime(workload_config,
experiment_trials)
preferred_results = experiment_results[preferred_performance_metric]
mean_result = mean_list(preferred_results)
perf_diff = mean_result - baseline_mean
if (perf_diff > 0.03 * baseline_mean) and optimize_for_lowest:
print 'Do nothing for optimize lowest'
elif (perf_diff <
-0.03 * baseline_mean) and optimize_for_lowest is False:
print 'Do nothing for optimize lowest'
else:
nimr_list.append(mr)
# Revert the Gradient schedule and provision resources accordingly
mr_revert_gradient_schedule = revert_mr_gradient_schedule(
redis_db, [mr], sys_config, stress_weight)
for change_mr in mr_revert_gradient_schedule:
resource_modifier.set_mr_provision(
change_mr, mr_revert_gradient_schedule[change_mr],
workload_config)
return nimr_list
def apply_pipeline_filter(redis_db, mr_working_set, experiment_iteration,
system_config, workload_config, filter_config):
logging.info('*' * 20)
logging.info('Applying Filtering Pipeline')
logging.info('Filter config is {}'.format(filter_config))
logging.info('MR working set is {}'.format(mr_working_set))
machine_type = system_config['machine_type']
pipeline_partitions = filter_config['pipeline_partitions']
stress_weight = filter_config['stress_amount']
experiment_trials = filter_config['filter_exp_trials']
pipelined_services = filter_config['pipeline_services']
pipeline_groups = []
logging.info('Pipelined services are {}'.format(pipelined_services))
# No specified pipelined services indicates that each pipeline is a service
if pipelined_services[0][0] == 'BY_SERVICE':
service_names = list(set([mr.service_name for mr in mr_working_set]))
for service_name in service_names:
mr_list = search_mr_working_set(mr_working_set, services)
pipeline_groups.append(mr_list)
elif pipelined_services[0][0] == 'RANDOM':
pipeline_groups = gen_mr_random_split(mr_working_set,
pipeline_partitions)
logging.info("The pipeline groups are being printed below: ")
for pipeline_group in pipeline_groups:
pipeline_group = [mr.to_string() for mr in pipeline_group]
logging.info('A pipeline is {}'.format(pipeline_group))
tbot_metric = workload_config['tbot_metric']
optimize_for_lowest = workload_config['optimize_for_lowest']
pipeline_index = 0
for pipeline in pipeline_groups:
change_mr_schedule = calculate_mr_gradient_schedule(
redis_db, pipeline, system_config, stress_weight)
# Simultaneously stress the MRs in a pipeline
for mr in change_mr_schedule:
resource_modifier.set_mr_provision(mr, change_mr_schedule[mr],
workload_config)
experiment_results = measure_runtime(workload_config,
experiment_trials)
exp_mean = mean_list(experiment_results[tbot_metric])
repr_str = str(pipeline_index)
tbot_datastore.write_filtered_results(redis_db, 'pipeline',
experiment_iteration, repr_str,
exp_mean)
# Revert the stressing
change_mr_schedule = revert_mr_gradient_schedule(
redis_db, pipeline, system_config, stress_weight)
for mr in change_mr_schedule:
resource_modifier.set_mr_provision(mr, change_mr_schedule[mr],
workload_config)
pipeline_index += 1
all_pipeline_score_list = tbot_datastore.get_top_n_filtered_results(
redis_db,
'pipeline',
experiment_iteration,
system_config,
optimize_for_lowest=optimize_for_lowest)
return all_pipeline_score_list, pipeline_groups
