def __init__(self):
self.output_filename = 'data/tmp'
self.num_epochs = 10000 # [x] number of training epochs
self.simu_len = 50 # number of jobs in a jobset (includes jobs of size 0)
self.num_ex = 1 # number of sequences
self.output_freq = 10 # interval for output and store parameters
self.num_seq_per_batch = 10 # number of sequences to compute baseline
self.episode_max_length = 510 # enforces an maximum horizon
self.num_q = 7 # [x] number of queues in the system
self.num_nw = 1 # [x] maximum allowed number of work in the queue
self.time_horizon = 80 # number of time steps in the graph
self.max_job_len = 15 # maximum duration of new jobs
self.max_job_size = 10 # maximum resource request of new work
self.backlog_size = 60 # backlog queue size
self.max_track_since_new = 10 # track how many time steps since last new jobs
self.q_num_cap = 40 # maximum number of distinct colors in current work graph
self.new_job_rate = 1.0 # lambda in new job arrival Poisson Process
self.job_aware = 0 # 1 => loader aware of pending job size
# 0 => loader oblivious to pending job size
self.discount = 1 # discount factor
# distribution for new job arrival
self.dist = job_distribution.Dist(self.num_q, self.max_job_len)
# neural network matrix input
self.network_input_width = self.num_q + self.job_aware
self.network_input_height = self.time_horizon
# neural network vectorized input
self.network_vec_input_dim = self.num_q + self.job_aware
# neural network outputs probability distribution over queues
self.network_output_dim = self.num_q
self.delay_penalty = -1 # penalty for delaying things in the current work screen
self.hold_penalty = -1 # penalty for holding things in the new work screen
self.dismiss_penalty = -1 # penalty for missing a job because the queue is full
self.num_frames = 1 # number of frames to combine and process
self.lr_rate = 0.001 # learning rate
self.rms_rho = 0.9 # for rms prop
self.rms_eps = 1e-9 # for rms prop
self.unseen = False # change random seed to generate unseen example
# supervised learning mimic policy
self.batch_size = 10
self.evaluate_policy_name = "LLQ"
def __init__(self):
self.output_filename = 'data/tmp'
self.num_epochs = 10000 # number of training epochs
self.simu_len = 10 # length of the busy cycle that repeats itself
self.num_ex = 1 # number of sequences
self.output_freq = 10 # interval for output and store parameters
self.num_seq_per_batch = 10 # number of sequences to compute baseline
self.episode_max_length = 200 # enforcing an artificial terminal
self.num_res = 2 # number of resources in the system
self.num_nw = 5 # maximum allowed number of work in the queue
self.time_horizon = 20 # number of time steps in the graph
self.max_job_len = 15 # maximum duration of new jobs
self.res_slot = 10 # maximum number of available resource slots
self.max_job_size = 10 # maximum resource request of new work
self.backlog_size = 60 # backlog queue size
self.max_track_since_new = 10 # track how many time steps since last new jobs
self.job_num_cap = 40 # maximum number of distinct colors in current work graph
self.new_job_rate = 0.7 # lambda in new job arrival Poisson Process
self.discount = 1 # discount factor
# distribution for new job arrival
self.dist = job_distribution.Dist(self.num_res, self.max_job_size,
self.max_job_len)
# graphical representation
assert self.backlog_size % self.time_horizon == 0 # such that it can be converted into an image
self.backlog_width = int(
math.ceil(self.backlog_size / float(self.time_horizon)))
self.network_input_height = self.time_horizon
self.network_input_width = \
(self.res_slot +
self.max_job_size * self.num_nw) * self.num_res + \
self.backlog_width + \
1 # for extra info, 1) time since last new job
# compact representation
self.network_compact_dim = (self.num_res + 1) * \
(self.time_horizon + self.num_nw) + 1 # + 1 for backlog indicator
self.network_output_dim = self.num_nw + 1 # + 1 for void action
self.delay_penalty = -1 # penalty for delaying things in the current work screen
self.hold_penalty = -1 # penalty for holding things in the new work screen
self.dismiss_penalty = -1 # penalty for missing a job because the queue is full
self.num_frames = 1 # number of frames to combine and process
self.lr_rate = 0.001 # learning rate
self.rms_rho = 0.9 # for rms prop
self.rms_eps = 1e-9 # for rms prop
self.unseen = False # change random seed to generate unseen example
# supervised learning mimic policy
self.batch_size = 10
self.evaluate_policy_name = "SJF"
def __init__(self):
self.output_filename = 'data/tmp'
self.num_epochs = 10000 # number of training epochs 将所有数据过几遍
self.simu_len = 10 # length of the busy cycle that repeats itself 生成的作业序列的长度
self.num_ex = 1 # number of sequences 作业序列条数
self.output_freq = 10 # interval for output and store parameters
self.num_seq_per_batch = 10 # number of sequences to compute baseline
self.episode_max_length = 200 # enforcing an artificial terminal
self.num_res = 2 # number of resources in the system 资源池的资源种类个数
self.num_nw = 5 # maximum allowed number of work in the queue 资源池的作业队列的最大长度
self.time_horizon = 20 # number of time steps in the graph graph的时间片长度（垂直长度）
self.max_job_len = 15 # maximum duration of new jobs 每个作业的作业最长持续时间
self.res_slot = 10 # maximum number of available resource slots graph的最大资源量（水平长度）
self.max_job_size = 10 # maximum resource request of new work 每个作业的最大资源需求量
self.backlog_size = 60 # backlog queue size 最大积压作业队列长度
self.max_track_since_new = 10 # track how many time steps since last new jobs # 距离上一次接收新作业的最大时间间隔
self.job_num_cap = 40 # maximum number of distinct colors in current work graph 当前资源池中最多存在颜色个数
self.new_job_rate = 0.7 # lambda in new job arrival Poisson Process 新作业到达率的泊松分布
self.discount = 1 # discount factor 折扣因子
self.num_machines = 2 # 资源池数量
# distribution for new job arrival 作业生成器
self.dist = job_distribution.Dist(self.num_res, self.max_job_size,
self.max_job_len)
# graphical representation 图像表示
assert self.backlog_size % self.time_horizon == 0 # such that it can be converted into an image
self.backlog_width = int(
math.ceil(self.backlog_size /
float(self.time_horizon))) # backlog的宽度
self.network_input_height = self.time_horizon # 神经网络的输入矩阵的高度
self.network_input_width = \
(self.res_slot * self.num_machines +
self.max_job_size * self.num_nw) * self.num_res + \
self.backlog_width + \
1 # for extra info, 1) time since last new job # 神经网络的输入矩阵的宽度【（单资源宽度+作业队列最大宽度）*资源种类个数+backlog宽度】
# compact representation 紧凑表示
self.network_compact_dim = (self.num_res + 1) * \
(self.time_horizon + self.num_nw) + 1 # + 1 for backlog indicator
self.network_output_dim = self.num_nw * self.num_machines + 1 # + 1 for void action
self.delay_penalty = -1 # penalty for delaying things in the current work screen
self.hold_penalty = -1 # penalty for holding things in the new work screen
self.dismiss_penalty = -1 # penalty for missing a job because the queue is full
self.num_frames = 1 # number of frames to combine and process
self.lr_rate = 0.001 # learning rate 学习率 决定参数移动到最优值的速度快慢
self.rms_rho = 0.9 # for rms prop
self.rms_eps = 1e-9 # for rms prop
self.unseen = False # change random seed to generate unseen example
# supervised learning mimic policy
self.batch_size = 10
self.evaluate_policy_name = "SJF"
