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setup_config.py
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import yaml
from job import *
import pandas as pd
import numpy as np
import os, pickle
from util import Event, Request
from client import *
from venn import *
from venn_job import *
from venn_client import *
setup_file = str(sys.argv[5]) if len(sys.argv) > 5 else None
config = None
if setup_file:
with open(setup_file, "r") as yamlfile:
config = yaml.load(yamlfile, Loader=yaml.FullLoader)
print("Read successful: ", config)
num_job = NUM_JOB = int(sys.argv[4]) if len(sys.argv)>4 else 2
RANDOMSEED = config['RANDOMSEED'] if config else 102
NUM_DAY = config['NUM_DAY'] if config else 5
NUM_WEEK = config['NUM_WEEK'] if config else 4
if str(sys.argv[1]).startswith("Random") or str(sys.argv[1]).startswith("LAS"):
NUM_WEEK *= 2
# elif not str(sys.argv[1]).startswith("AMG"):
# NUM_WEEK += 10
print("NUM_WEEK: ", NUM_WEEK)
client_file = config['client_file'] if config else 'trace/fedscale_clients_7000000_3_info.csv'
eligibility_file = config['eligibility_file'] if config else 'trace/baseline_eligibility_3skew'
job_config_file = config['job_config_file'] if 'job_config_file' in config else 'config/job_config.yml'
random.seed(RANDOMSEED)
np.random.seed(RANDOMSEED)
apple_job = ['AppleJob', 'DecAppleJob']
google_job = ['Job', 'AgnosticJob', 'GoogleJob', 'DecentralizedJob', 'GoogleJob']
async_job = ['PapayaJob', 'DecPapayaJob', 'PapayaJob']
amg_job_name = ['GoogleJob', 'AppleJob', 'PapayaJob' ]
def load_device_capacity(file_path = 'trace/client_device_capacity'):
global_client_profile = {}
if os.path.exists(file_path):
with open(file_path, 'rb') as fin:
# {clientId: [computer, bandwidth]}
global_client_profile = pickle.load(fin)
return global_client_profile
def load_device_eligibility(eligibility_file):
with open(eligibility_file, 'rb') as config_dictionary_file:
eligibility = pickle.load(config_dictionary_file)
return eligibility
def load_device_state(client_file, eligibility_file, client_type , days ):
client_capacity = load_device_capacity()
client_eligibility = load_device_eligibility(eligibility_file)
num_cap = len(client_capacity)
num_eli = len(client_eligibility)
avg_comp = 78
avg_comm = 13736
weeks = NUM_WEEK
client_event_list= []
client_trace = pd.read_csv(client_file)
client_type = eval(client_type)
print(f"Using {client_type}")
for i, (ind, client) in enumerate(client_trace.sort_values(by=['start']).iterrows()):
# for i, (ind, client) in enumerate(client_trace.iterrows() ):
if client['start'] > days * 86400:
break
# if client['start'] < 120: # or client['end'] - client['start'] < 20:
# continue
comm = client_capacity[i % num_cap + 1]['communication'] / avg_comm
comp = client_capacity[i % num_cap + 1]['computation'] / avg_comp
# c = client_type(i, client['start'], client['end'], comp, comm, client_eligibility[i%num_eli])
# client_event_list.append( Event(client['start'], 'CHECKIN', i, c) )
for j in range(weeks):
# TODO: client online period is too short; remove some short-lived clients
# while manually increase their online period to ensure enough traffic
start = client['start'] + j * 432000
c = client_type( i*weeks+j, start, client['end'] + 80 + j * 432000, comp, comm, client_eligibility[i % num_eli])
client_event_list.append(Event(start, 'CHECKIN', i*weeks+j, c))
# c = client_type( -i*weeks+j, client['start'] + j * 432000, client['end'] + 80 + j * 432000, comp, comm, client_eligibility[i % num_eli])
# client_event_list.append(Event(client['start'] + j * 432000, 'CHECKIN', -i*weeks+j, c))
# if i > 10000 :
# break
if i % 100000 == 0:
print(f'Checkin {i*weeks} clients')
return client_event_list
job_minresponse_list = [0.8 for _ in range(num_job)]
comm_time = 10
def generate_job_by_config(num_job, job_type):
with open(job_config_file, "r") as configfile:
job_config = yaml.load(configfile, Loader=yaml.FullLoader)
job_deadline_list = [0 for _ in range(num_job)] #
request_list = []
job_list = []
job_request_list = []
if 'arrival_interval' in job_config:
arrival_interval = job_config['arrival_interval']
else:
arrival_interval = 1800
start_time = 3600
job_start_list = [start_time]
for i in range(num_job - 1):
job_start_list.append(job_start_list[-1] + np.random.exponential(scale=arrival_interval))
num_job_req = len(job_config['job_requirement'])
jobs = job_config['jobs']
job_id_prob = np.array( config['job_id_prob'])
job_type_prob = config['job_type_prob']
job_id_prob = job_id_prob/ sum (job_id_prob)
generate_jobid_by_prob = np.random.choice([*range(len(jobs))], num_job, p = job_id_prob )
print("Random generate job id: ", generate_jobid_by_prob)
jtype = job_type
if jtype in async_job:
job_deadline_list = [0 for _ in range(num_job)] #
for i, job_id in enumerate(generate_jobid_by_prob):
print(jobs[job_id])
if job_type == 'MixedJob':
if jobs[job_id]['config']['job_type'] == 'sync':
jtype = np.random.choice(amg_job_name[:2], 1, p = job_type_prob[:2] )[0]
else:
jtype = 'PapayaJob'
num_part = jobs[job_id]['config']['participants']
if jtype in google_job:
job_deadline_list[i] = jobs[job_id]['config']['deadline']
job_workload = jobs[job_id]['config']['workload']
eligibility = jobs[job_id]['config']['eligibility'] if 'eligibility' in jobs[job_id]['config'] else i%num_job_req
job_request_list += [Request(job_deadline_list[i], num_part,
job_minresponse_list[i], job_workload, comm_time,
eligibility)]
job_list += [eval(jtype)(i, jtype, job_request_list[i], jobs[job_id]['config']['rounds'],
job_start_list[i], jobs[job_id]['config']['concurrency'])]
# concurrency only for async --> is actually buffer size
for job in job_list:
request_list += job.generate_round_event()
request_list += [Event(NUM_DAY * 86400 * NUM_WEEK, 'END', None, None)]
return request_list, job_list, job_request_list
def generate_job_request(num_job, job_type , days = 5):
if config: # MixedJob / config
return generate_job_by_config(num_job, job_type)