mp_test_fixed_lg.py

import os
import numpy as np
from itertools import count
from multiprocessing import Pool, cpu_count

from config.test import *
from envs.datacenter_env.env import DatacenterEnv
from utils import *


class LG:
    def select_action(self, obs):
        _, job_run_time, _, machines_run_time, _, action_mask = obs
        gap = np.abs(machines_run_time - job_run_time)
        gap[action_mask == False] = 1e9
        action = np.argmin(gap)
        return action


def test_one_path(args, seq_index, data_save_path, fig_save_path):
    print("start test seq_index: ", seq_index)
    # init agent
    agent = LG()

    # init env
    env = DatacenterEnv(args)
    env.seq_index = seq_index

    # start test
    obs = env.reset()
    for _ in count():
        # select and perform an action
        action = agent.select_action(obs)
        # execute action
        next_obs, _, done, _ = env.step(action)
        # move to the next state
        obs = next_obs

        if done:
            break

    # save test result
    # save not run to end data
    machines_occupancy_rate = np.array(env.machines_occupancy_rate_record)
    np.save(
        os.path.join(data_save_path, f"occupancy_rate_{seq_index}.npy"),
        machines_occupancy_rate,
    )
    machines_finish_time_record = np.array(env.machines_finish_time_record)
    np.save(
        os.path.join(data_save_path, f"finish_time_{seq_index}.npy"),
        machines_finish_time_record,
    )

    # print mean std and mean run time
    machines_occupancy_std = np.std(machines_occupancy_rate * args.res_capacity, axis=1)
    machines_occupancy_mean_std = np.mean(machines_occupancy_std, axis=1)
    std_fitness = np.mean(machines_occupancy_mean_std)
    runtime_fitness = np.mean(machines_finish_time_record)
    print(f"std_fitness {std_fitness} runtime_fitness {runtime_fitness}")

    # save run to end data
    env.run_to_end()
    machines_occupancy_rate = np.array(env.machines_occupancy_rate_record)
    np.save(
        os.path.join(data_save_path, f"end_occupancy_rate_{seq_index}.npy"),
        machines_occupancy_rate,
    )
    machines_finish_time_record = np.array(env.machines_finish_time_record)
    np.save(
        os.path.join(data_save_path, f"end_finish_time_{seq_index}.npy"),
        machines_finish_time_record,
    )

    for i in range(4):
        data = machines_occupancy_rate[:, :, i]
        save_name = os.path.join(fig_save_path, "use_rate", f"use_rate_e{seq_index}_{i}.png")
        plot_mutil_lines_chart(
            data,
            save_name=save_name,
            xlabel="time",
            ylabel="utilization",
            title="Container Resource Utilization",
        )

    save_name = os.path.join(fig_save_path, "finish_time", f"finish_time_e{seq_index}.png")
    plot_mutil_lines_chart(
        machines_finish_time_record,
        save_name=save_name,
        xlabel="time",
        ylabel="remaining time",
        title="Container Remaining Time",
    )

    return std_fitness, runtime_fitness, env.job_num


if __name__ == "__main__":
    args = parse_args()
    args.method = "lg"
    save_dir = os.path.join(
        args.save_path,
        args.method,
        args.tag,
    )

    model_save_path = os.path.join(save_dir, "models")
    fig_save_path = os.path.join(save_dir, "fig")
    data_save_path = os.path.join(save_dir, "data")
    os.makedirs(data_save_path, exist_ok=True)
    os.makedirs(os.path.join(fig_save_path, "use_rate"), exist_ok=True)
    os.makedirs(os.path.join(fig_save_path, "finish_time"), exist_ok=True)
    os.makedirs(model_save_path, exist_ok=True)
    os.makedirs(fig_save_path, exist_ok=True)

    # mutil process
    mutil_process = []
    pool = Pool(cpu_count())
    for i in range(args.job_seq_num):
        one_process = pool.apply_async(test_one_path, args=(args, i, data_save_path, fig_save_path))
        mutil_process.append(one_process)

    pool.close()
    pool.join()

    # caculate mean performent
    fitness_record = []
    job_num_list = []
    for p in mutil_process:
        std_fitness, runtime_fitness, job_num = p.get()
        job_num_list.append(job_num)
        fitness_record.append((std_fitness, runtime_fitness))
    fitness_record = np.array(fitness_record)
    mean_fitness = np.mean(fitness_record, axis=0)
    std_fitness = np.std(fitness_record, axis=0)
    print(job_num_list)
    np.save(os.path.join(data_save_path, "job_num.npy"), np.array(job_num))
    print(
        "mean std fitness: {:.4f} mean runtime fitness: {:.4f}".format(
            mean_fitness[0], mean_fitness[1]
        )
    )
    print(
        "std std fitness: {:.4f} std runtime fitness: {:.4f}".format(std_fitness[0], std_fitness[1])
    )
    print("done")