def observe(self):
obs_arr = []
# load on each server
for server in self.servers:
# queuing work
load = sum(j.size for j in server.queue)
if server.curr_job is not None:
# remaining work currently being processed
load += server.curr_job.finish_time - self.wall_time.curr_time
# if the load is larger than observation threshold
# report a warning
if load > self.obs_high[server.server_id]:
logger.warn('Server ' + str(server.server_id) + ' at time ' +
str(self.wall_time.curr_time) + ' has load ' + str(load) +
' larger than obs_high ' + str(self.obs_high[server.server_id]))
load = self.obs_high[server.server_id]
obs_arr.append(load)
# incoming job size
if self.incoming_job is None:
obs_arr.append(0)
else:
if self.incoming_job.size > self.obs_high[-1]:
logger.warn('Incoming job at time ' + str(self.wall_time.curr_time) +
' has size ' + str(self.incoming_job.size) +
' larger than obs_high ' + str(self.obs_high[-1]))
obs_arr.append(self.obs_high[-1])
else:
obs_arr.append(self.incoming_job.size)
obs_arr = np.array(obs_arr)
assert self.observation_space.contains(obs_arr)
return obs_arr
def step(self, action):
# action is a permutation of identity matrix
assert self.action_space.contains(action)
# action corresponding to one of the permutation
port_mapping = self.all_mappings[action]
# assign packet to the output port
for (i, p) in enumerate(port_mapping):
self.queue_occupancy[i, p] = max(self.queue_occupancy[i, p] - 1, 0)
# reamining total queue length
reward = -np.sum(self.queue_occupancy)
# never ending environment
done = False
# sample new traffic
incoming_traffic = self.sample_from_bistochastic_matrix()
self.queue_occupancy += incoming_traffic
# cap the observation
if np.any(self.queue_occupancy > config.ss_state_max_queue):
obs_queue = np.minimum(self.queue_occupancy,
config.ss_state_max_queue)
logger.warn(
'Queue occupancy is clipped since it exceeds max queue value '
+ str(config.ss_state_max_queue))
else:
obs_queue = self.queue_occupancy
# current queue occupancy in the observation space
assert self.observation_space.contains(obs_queue)
return obs_queue, reward, done, {}
def __init__(self,
low=None,
high=None,
struct=None,
shape=None,
dtype=None):
"""
Two kinds of valid input:
Box(low=-1.0, high=1.0, shape=(3,4)) # low and high are scalars, and shape is provided
Box(low=np.array([-1.0,-2.0]), high=np.array([2.0,4.0])) # low and high are arrays of the same shape
"""
if shape is None:
assert low.shape == high.shape
shape = low.shape
else:
assert np.isscalar(low) and np.isscalar(high)
low = low + np.zeros(shape)
high = high + np.zeros(shape)
if dtype is None: # Autodetect type
if (high == 255).all():
dtype = np.uint8
else:
dtype = np.float32
logger.warn(
"park.spaces.Box autodetected dtype as {}. Please provide explicit dtype."
.format(dtype))
self.low = low.astype(dtype)
self.high = high.astype(dtype)
#core.Space.__init__(self, struct, shape, dtype)
space.Box.__init__(self, low, high, shape, dtype)
def clip_obs(obs, obs_low, obs_high):
# TODO: this supports 1D only, need to add a iterator in each space type
assert len(obs.shape) == 1
# fit in observation space
for i in range(obs.shape[0]):
if obs[i] > obs_high[i]:
logger.warn('Observation at index ' + str(i) + ' has value ' +
str(obs_arr[i]) + ', which is larger than obs_high ' +
str(obs_high[i]))
obs[i] = obs_high[i]
if obs[i] < obs_low[i]:
logger.warn('Observation at index ' + str(i) + ' has value ' +
str(obs_arr[i]) + ', which is lower than obs_low ' +
str(obs_low[i]))
obs[i] = obs_low[i]
def observe(self):
if self.chunk_idx < self.total_num_chunks:
valid_chunk_idx = self.chunk_idx
else:
valid_chunk_idx = 0
if self.past_action is not None:
valid_past_action = self.past_action
else:
valid_past_action = 0
# network throughput of past chunk, past chunk download time,
# current buffer, number of chunks left and the last bitrate choice
obs_arr = [self.past_chunk_throughputs[-1],
self.past_chunk_download_times[-1],
self.buffer_size,
self.total_num_chunks - self.chunk_idx,
valid_past_action]
# current chunk size of different bitrates
obs_arr.extend(self.chunk_sizes[i][valid_chunk_idx] for i in range(6))
# fit in observation space
for i in range(len(obs_arr)):
if obs_arr[i] > self.obs_high[i]:
logger.warn('Observation at index ' + str(i) +
' at chunk index ' + str(self.chunk_idx) +
' has value ' + str(obs_arr[i]) +
', which is larger than obs_high ' +
str(self.obs_high[i]))
obs_arr[i] = self.obs_high[i]
obs_arr = np.array(obs_arr)
assert self.observation_space.contains(obs_arr)
return obs_arr
def seed(self, seed=None):
"""
Sets the seed for this env's random number generator(s).
"""
logger.warn('Could not seed environment ' + self.metadata['env.name'])
return
