def worker():
Thread(target=init_vegeta).start()
Thread(target=init_vegeta_v2).start()
time.sleep(10)
print("kill vegeta")
kill_vegeta(CURRENT_TRAFFIC)
kill_vegeta(CURRENT_TRAFFIC_V2)
time.sleep(1)
r1_throughput = get_throughput('./servicec.txt')
r2_throughput = get_throughput('./servicec2.txt')
current_overall_throughput = r1_throughput + r2_throughput
# lstm part
# current_stat_list = generate_metric(5, query_dict)
# latency = get_mean_latency('./servicea.txt')
# empty_stat_list = []
# for i in range(len(current_stat_list)):
# current_stat_list[i]["mean_latency"] = latency
# empty_stat_list += [float(i) for i in list(current_stat_list[i].values())]
# new_limit = lstm_model(LSTM_MODEL_DIR, empty_stat_list)
# exit()
# get current stat
current_stat = generate_metric(1, query_dict)[0]
current_stat["mean_latency"] = get_mean_latency('./servicec.txt')
current_stat_val = [float(i) for i in list(current_stat.values())]
new_limit = r1_c_predictor(current_stat_val)
# new_limit = fc_model(FC_MODEL_DIR, current_stat_val)
new_limit = int(new_limit)
if new_limit < 0:
return
print("new_limit: ", str(new_limit))
current_limit_dict = get_all_keys()
old_limit = current_limit_dict[CURRENT_REQUEST_SERVICE]
current_limit_dict[CURRENT_REQUEST_SERVICE] = new_limit
predicted_throughput = checkerInfer(
[float(i) for i in list(current_limit_dict.values())])
print("Trying to edit descriptor " + CURRENT_REQUEST_SERVICE + " from " +
str(old_limit) + " To " + str(new_limit))
print("predicted_throughput: " + str(predicted_throughput))
print("current_throughput: " + str(current_overall_throughput))
if predicted_throughput >= current_overall_throughput:
print("Edit Sucess")
edit_key(CURRENT_REQUEST_SERVICE, new_limit)
else:
print("Edit Fail")
Thread(target=init_vegeta_v2).start()
time.sleep(20)
print("kill vegeta")
kill_vegeta(CURRENT_TRAFFIC)
kill_vegeta(CURRENT_TRAFFIC_V2)
time.sleep(1)
r1_throughput = get_throughput('./out.txt')
r2_throughput = get_throughput('./out2.txt')
current_limit_dict = get_all_keys()
for key, val in current_limit_dict.items():
one_record[key] = val
one_record["r1_throughput"] = r1_throughput
one_record["r2_throughput"] = r2_throughput
one_record["r1_mean_latency"] = get_mean_latency('./out.txt')
one_record["r2_mean_latency"] = get_mean_latency('./out2.txt')
one_record["throughput"] = r1_throughput + r2_throughput
# list_of_dict.append(one_record)
print("generate " + str(i)+ " th sample ..................")
print(one_record)
appendlist2csv(one_record, "checkerDatasetv2.csv")
# nestedlist2csv(list_of_dict, "checkerDataset" + time.asctime(time.localtime(time.time())) +".csv")
# nestedlist2csv(list_of_dict, "checkerDatasetv2.csv")
kill_docker_compose()
def appendLatency(sourceFile, outputFile, latency):
sourceFile = sourceFile
df = pd.read_csv(sourceFile, low_memory=False)
df['mean_latency'] = latency
print("append latency success!!")
columns_val = list(df.columns.values)
df.to_csv(outputFile, columns=columns_val, index=0, header=1)
if __name__ == "__main__":
# generate dataset
list_of_dict = generate_dataet(NUMOFRECORD, query_dict)
nestedlist2csv(list_of_dict,
CURRENT_REQUEST_SERVICE + "_" + current_limit_str + ".csv")
print("kill vegeta and stop docker compose")
kill_vegeta(CURRENT_TRAFFIC)
kill_vegeta(CURRENT_TRAFFIC_V2)
kill_docker_compose()
latency = get_mean_latency("./out.txt")
sourceFile = CURRENT_REQUEST_SERVICE + "_" + current_limit_str + ".csv"
outputFile = sourceFile
appendLatency(sourceFile, outputFile, latency)
def worker():
Thread(target=init_vegeta).start()
Thread(target=init_vegeta_v2).start()
time.sleep(10)
print("kill vegeta")
kill_vegeta(CURRENT_TRAFFIC)
kill_vegeta(CURRENT_TRAFFIC_V2)
time.sleep(1)
r1_throughput = get_throughput('./servicecb.txt')
r2_throughput = get_throughput('./servicecb2.txt')
current_overall_throughput = r1_throughput + r2_throughput
# lstm part
# current_stat_list = generate_metric(5, query_dict)
# latency = get_mean_latency('./servicea.txt')
# empty_stat_list = []
# for i in range(len(current_stat_list)):
# current_stat_list[i]["mean_latency"] = latency
# empty_stat_list += [float(i) for i in list(current_stat_list[i].values())]
# r1_b_new_limit = lstm_model("models/dl/r1_service_b_lstm", empty_stat_list)
# exit()
# get current stat
current_stat = generate_metric(1, query_dict)[0]
current_stat["mean_latency"] = get_mean_latency('./servicecb.txt')
current_stat_val = [float(i) for i in list(current_stat.values())]
r1_b_new_limit = r1_b_predictor(current_stat_val)
# r1_b_new_limit = fc_model("models/dl/r1_service_b_fc", current_stat_val)
r1_b_new_limit = int(r1_b_new_limit)
if r1_b_new_limit < 0:
return
current_stat["mean_latency"] = get_mean_latency('./servicecb2.txt')
current_stat_val = [float(i) for i in list(current_stat.values())]
r2_b_new_limit = r2_b_predictor(current_stat_val)
# r2_b_new_limit = fc_model("models/dl/r2_service_b_fc", current_stat_val)
# r2_b_new_limit = lstm_model("models/dl/r2_service_b_lstm", empty_stat_list)
r2_b_new_limit = int(r2_b_new_limit)
if r2_b_new_limit < 0:
return
current_limit_dict = get_all_keys()
old_r1_service_b = current_limit_dict["r1_service_b"]
old_r2_service_b = current_limit_dict["r2_service_b"]
current_limit_dict["r1_service_b"] = r1_b_new_limit
current_limit_dict["r2_service_b"] = r2_b_new_limit
predicted_throughput = checkerInfer(
[float(i) for i in list(current_limit_dict.values())])
print("predicted_throughput: " + str(predicted_throughput))
print("current_throughput: " + str(current_overall_throughput))
print("Trying to edit descriptor " + "r1_service_b" + " from " +
str(old_r1_service_b) + " To " + str(r1_b_new_limit))
print("Trying to edit descriptor " + "r2_service_b" + " from " +
str(old_r2_service_b) + " To " + str(r2_b_new_limit))
if predicted_throughput >= current_overall_throughput:
print("edit success")
edit_key("r1_service_b", r1_b_new_limit)
edit_key("r2_service_b", r2_b_new_limit)
else:
print("edit fail, try to apply partial rate")
do_priority_part(old_r1_service_b, old_r2_service_b, r1_b_new_limit,
r2_b_new_limit)