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dfaas_env.py
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dfaas_env.py
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# This file contains the DFaaS multi-agent environment and associated callbacks.
#
# Actually contains three (or more) classes:
#
# 1. The DFaaS asymmetric environment (only one node can forward),
# 2. The DFaaS (symmetrical) environment,
# 3. The callbacks used for both environments.
#
# This file may contain additional classes for specialized environments and
# callbacks during experimentation.
import gymnasium as gym
import logging
from pathlib import Path
import pandas as pd
import numpy as np
from ray.rllib.env.multi_agent_env import MultiAgentEnv
from ray.rllib.utils.spaces.simplex import Simplex
from ray.tune.registry import register_env
from ray.rllib.algorithms.callbacks import DefaultCallbacks
# Initialize logger for this module.
logging.basicConfig(format="%(asctime)s %(levelname)s %(filename)s:%(lineno)d -- %(message)s", level=logging.DEBUG)
_logger = logging.getLogger(Path(__file__).name)
def _reward_no_fw(action, excess, queue):
"""Reward function for the agents without forwarding action.
The reward is based on:
- The action, a 2-length tuple containing the number of requests to
process locally and to reject.
- The excess, a 1-length tuple containing the number of local requests
that exceed the queue capacity.
- The queue, a 2-length tuple containing the status of the queue (0 is
empty) and the maximum capacity of the queue.
The reward returned is in the range [0, 1]."""
assert len(action) == 2, "Expected (local, reject)"
assert len(excess) == 1, "Expected (local_excess)"
assert len(queue) == 2, "Expected (queue_status, queue_max_capacity)"
reqs_total = sum(action)
reqs_local, reqs_reject = action
local_excess = excess[0]
queue_status, queue_max = queue
if reqs_total == 0:
return 1.
if local_excess > 0:
return 1 - (local_excess / reqs_local)
free_slots = queue_max - queue_status
assert free_slots >= 0
# Calculate the number of excess reject requests.
if free_slots > reqs_reject:
reject_excess = reqs_reject
else: # reqs_reject >= free_slots
valid_reject = reqs_reject - free_slots
assert valid_reject >= 0
reject_excess = reqs_reject - valid_reject
assert reject_excess >= 0
return 1 - (reject_excess / reqs_total)
def _reward_fw(action, excess, queue):
"""Reward function for the agents with forwarding action.
The reward is based on:
- The action, a 3-length tuple containing the number of requests to
process locally, to forward, and to reject.
- The excess, a 2-length tuple containing the local requests that
exceed the queue capacity and the forwarded requests that were
rejected by the other agent.
- The queue, a 2-length tuple containing the status of the queue (0
is empty) and the maximum capacity of the queue.
The reward returned is in the range [0, 1]."""
assert len(action) == 3, "Expected (local, forward, reject)"
assert len(excess) == 2, "Expected (local_excess, forward_reject)"
assert len(queue) == 2, "Expected (queue_status, queue_max_capacity)"
reqs_total = sum(action)
reqs_local, reqs_forward, reqs_reject = action
local_excess, forward_reject = excess
queue_status, queue_max = queue
if reqs_total == 0:
return 1.
free_slots = queue_max - queue_status
assert free_slots >= 0
# Calculate the number of excess forward requests.
#
# The forwarded and rejected requests must be excluded from the count
# because they are considered separately.
reqs_forward -= forward_reject
if free_slots > reqs_forward:
forward_excess = reqs_forward
else: # reqs_forward >= free_slots
valid_forward = reqs_forward - free_slots
assert valid_forward >= 0
forward_excess = reqs_forward - valid_forward
# Calculate the number of excess reject requests.
free_slots = free_slots - forward_excess
assert free_slots >= 0
if free_slots > reqs_reject:
valid_reject = 0
reject_excess = reqs_reject
else: # reqs_reject >= free_slots
valid_reject = reqs_reject - free_slots
assert valid_reject >= 0
reject_excess = reqs_reject - valid_reject
# Calculate the number of rejected requests that could have been forwarded.
if forward_reject == 0 and valid_reject > 0:
# Assume that all rejected requests could have been forwarded because no
# forwarded requests were rejected.
reject_excess += valid_reject
valid_reject = 0
assert local_excess >= 0 \
and forward_reject >= 0 \
and forward_excess >= 0 \
and reject_excess >= 0
wrong_reqs = local_excess + forward_reject + forward_excess + reject_excess
assert wrong_reqs <= reqs_total, f"({local_excess = } + {forward_reject = } + {forward_excess = } + {reject_excess = }) <= {reqs_total}"
return 1 - (wrong_reqs / reqs_total)
class DFaaS_BASE(MultiAgentEnv):
# String representing the type of this environment.
type = "BASE"
# Keys contained in the additional_info dictionary.
#
# To use the same callbacks for the three environments, some keys must be
# present, but they will be empty. The 'type' attribute can be used to skip
# the useless keys.
info_keys = {"observation_input_requests": np.int32,
"observation_queue_capacity": np.int32,
"action_local": np.int32,
"action_forward": np.int32,
"action_reject": np.int32,
"excess_local": np.int32,
"excess_forward_reject": np.int32,
"queue_status_pre_forward": np.int32,
"queue_status_post_forward": np.int32,
"reward": np.float32}
def __init__(self, config={}):
# Number and IDs of the agents in the DFaaS network.
self.agents = 2
self.agent_ids = ["node_0", "node_1"]
# String representing the type of this environment.
self.suffix = "BASE"
# This attribute is required by Ray and must be a set. I use the list
# version instead in this environment.
self._agent_ids = set(self.agent_ids)
# It is the possible max and min value for the reward returned by the
# step() call.
self.reward_range = (.0, 1.)
# The size of each agent's local queue. The queue can be filled with
# requests to be processed locally.
self.queue_capacity_max = {
"node_0": config.get("queue_capacity_max_node_0", 100),
"node_1": config.get("queue_capacity_max_node_1", 100),
}
# Provide full (preferred format) observation- and action-spaces as
# Dicts mapping agent IDs to the individual agents' spaces.
self._action_space_in_preferred_format = True
self.action_space = gym.spaces.Dict({
# Distribution of how many requests are processed locally and
# rejected.
agent: Simplex(shape=(2,)) for agent in self.agent_ids
})
self._obs_space_in_preferred_format = True
self.observation_space = gym.spaces.Dict({
agent: gym.spaces.Dict({
# Number of input requests to process for a single step.
"input_requests": gym.spaces.Box(low=0, high=150, dtype=np.int32),
# Queue capacity (currently a constant).
"queue_capacity": gym.spaces.Box(low=0, high=self.queue_capacity_max[agent], dtype=np.int32),
}) for agent in self.agent_ids
})
# Number of steps in the environment. The default is one step for every
# 5 minutes of a 24-hour day.
self.max_steps = config.get("max_steps", 288)
# Type of input requests.
self.input_requests_type = config.get("input_requests_type", "synthetic-sinusoidal")
match self.input_requests_type:
case "synthetic":
print("WARN: 'synthetic' type deprecated, use 'synthetic-sinusoidal'")
pass
case "synthetic-sinusoidal":
pass
case "synthetic-normal":
pass
case "real":
assert self.max_steps == 288, f"With {self.input_requests_type = } only 288 max_steps are supported"
case _:
assert False, f"Unsupported {self.input_requests_type = }"
# Is the env created for evaluation only? If so, the input requests may
# differ from the training ones.
self.evaluation = config.get("evaluation", False)
super().__init__()
def get_config(self):
"""Returns a dictionary with the current configuration of the
environment."""
config = {}
config["queue_capacity_max_node_0"] = self.queue_capacity_max["node_0"]
config["queue_capacity_max_node_1"] = self.queue_capacity_max["node_1"]
config["max_steps"] = self.max_steps
config["input_requests_type"] = self.input_requests_type
return config
def reset(self, *, seed=None, options=None):
# Current step.
self.current_step = 0
# If seed is given, overwrite the master seed. Ray will give the seed in
# reset() only when it creates the environment for each rollout worker
# (and local worker). Each worker has a specific seed.
if seed is not None:
# The master seed for the RNG. This is used in each episode (each
# "reset()") to create a new RNG that will be used for generating
# input requests.
#
# Using the master seed make sure to generate a reproducible
# sequence of seeds.
self.master_seed = seed
self.master_rng = np.random.default_rng(seed=self.master_seed)
# Seed used for this episode.
if isinstance(options, dict) and "override_seed" in options:
# By default, the seed is generated. But can be overrided (usually
# on manual calls, not from Ray).
self.seed = options["override_seed"]
else:
iinfo = np.iinfo(np.uint32)
self.seed = self.master_rng.integers(0, high=iinfo.max, size=1)[0]
# Create the RNG used to generate input requests.
self.rng = np.random.default_rng(seed=self.seed)
self.np_random = self.rng # Required by the Gymnasium API
# Generate all input requests for the environment.
limits = {}
for agent in self.agent_ids:
limits[agent] = {
"min": self.observation_space[agent]["input_requests"].low.item(),
"max": self.observation_space[agent]["input_requests"].high.item()
}
if self.input_requests_type == "synthetic-sinusoidal" or self.input_requests_type == "synthetic":
self.input_requests = _synthetic_sinusoidal_input_requests(self.max_steps,
self.agent_ids,
limits,
self.rng)
elif self.input_requests_type == "syntethic-normal":
self.input_requests = _synthetic_normal_input_requests(self.max_steps,
self.agent_ids,
limits,
self.rng)
else: # "real"
retval = _real_input_requests(self.max_steps, self.agent_ids,
limits, self.rng, self.evaluation)
self.input_requests = retval[0]
# Special attribute, not returned in the observation: contains the
# hashes of the selected input requests. It is used by the
# callbacks.
self.input_requests_hashes = retval[1]
# Queue state for each agent (number of requests to process locally).
# The queues start empty (max capacity) and can be full.
self.queue = {agent: 0 for agent in self.agent_ids}
self.last_info = None # Required by _build_observation().
obs = self._build_observation()
# For each reset() and step() call, the info dictionary is stored in an
# attribute and is not returned. The caller can access this attribute
# directly (usually at the end of the episode).
#
# To update the dictionary, a private function is called at the end of
# reset() and call().
self.additional_info = None
self._additional_info(obs)
return obs, {}
def step(self, action_dict):
# Action for node_0.
input_requests_0 = self.input_requests["node_0"][self.current_step]
# Convert the action distribution (a distribution of probabilities) into
# the number of requests to locally process and to reject.
action_0 = _convert_distribution_no_fw(input_requests_0, action_dict["node_0"])
# node_1
input_requests_1 = self.input_requests["node_1"][self.current_step]
action_1 = _convert_distribution_no_fw(input_requests_1, action_dict["node_1"])
# We have the actions, now update the environment state.
info_work = self._manage_workload(action_0, action_1)
# Calculate the reward for both agents.
rewards = {}
rewards["node_0"] = _reward_no_fw(action_0,
(info_work["node_0"]["local_excess"],),
(info_work["node_0"]["queue_status"], self.queue_capacity_max["node_0"]))
rewards["node_1"] = _reward_no_fw(action_1,
(info_work["node_1"]["local_excess"],),
(info_work["node_1"]["queue_status"], self.queue_capacity_max["node_1"]))
# Make sure the reward is of type float.
for agent in self.agent_ids:
rewards[agent] = float(rewards[agent])
# Required by _build_observation().
self.last_info = {
"action": {"node_0": action_0, "node_1": action_1},
"rewards": rewards,
"workload": info_work
}
# Go to the next step.
self.current_step += 1
# Free the queues (for now...).
self.queue = {agent: 0 for agent in self.agent_ids}
# Each key in the terminated dictionary indicates whether an individual
# agent has terminated. There is a special key "__all__" which is true
# only if all agents have terminated.
terminated = {agent: False for agent in self.agent_ids}
if self.current_step == self.max_steps:
# We are past the last step: nothing more to do.
terminated = {agent: True for agent in self.agent_ids}
terminated["__all__"] = all(terminated.values())
# Truncated is always set to False because it is not used.
truncated = {agent: False for agent in self.agent_ids}
truncated["__all__"] = False
if self.current_step < self.max_steps:
obs = self._build_observation()
else:
# Return a dummy observation because this is the last step.
obs = self.observation_space_sample()
# Update the additional_info dictionary.
self._additional_info(obs, self.last_info["action"], rewards, info_work)
return obs, rewards, terminated, truncated, {}
def _build_observation(self):
"""Builds and returns the observation for the current step."""
assert self.current_step < self.max_steps
# Initialize the observation dictionary.
obs = {agent: {} for agent in self.agent_ids}
# Set observation values for the agents.
for agent in self.agent_ids:
# The queue capacity is always a fixed value for now.
obs[agent]["queue_capacity"] = np.array([self.queue_capacity_max[agent]], dtype=np.int32)
input_requests = self.input_requests[agent][self.current_step]
obs[agent]["input_requests"] = np.array([input_requests], dtype=np.int32)
return obs
def _manage_workload(self, action_0, action_1):
"""Fills the agent queues with the requests provided by the actions and
returns a dictionary containing information for the two agents:
- For "node_0": the number of excess local requests,
- For "node_1": the number of excess local requests.
"""
local_0, _ = action_0
local_1, _ = action_1
info = {agent: {} for agent in self.agent_ids}
# Helper function.
def fill_queue(agent, requests):
"""Fill the queue of the specified agent with the specified number
of requests. Returns the number of excess requests (requests that
cannot be added to the queue)."""
excess = 0
free_slots = self.queue_capacity_max[agent] - self.queue[agent]
if free_slots >= requests:
# There are enough slots in the queue to handle the number of
# requests specified by the action.
self.queue[agent] += requests
else:
# The requests specified by the action do not have enough slots
# in the queue to be processed locally. So we have a number of
# requests that are overflowing.
excess = requests - free_slots
# However, use the available slots.
self.queue[agent] += requests - excess
return excess
# Local processing for node_0.
info["node_0"]["local_excess"] = fill_queue("node_0", local_0)
info["node_0"]["queue_status"] = self.queue["node_0"]
# Local processing for node_1.
info["node_1"]["local_excess"] = fill_queue("node_1", local_1)
info["node_1"]["queue_status"] = self.queue["node_1"]
return info
def _additional_info(self, obs, action=None, rewards=None, info_work=None):
"""Update the additional_info dictionary with the current step."""
# Initialize the additional_info dictionary with all the NumPy arrays.
if self.additional_info is None:
self.additional_info = {}
for key in self.info_keys:
self.additional_info[key] = {}
for agent in self.agent_ids:
self.additional_info[key][agent] = np.zeros(self.max_steps, dtype=self.info_keys[key])
# Update the additional_info dictionary.
for agent in self.agent_ids:
# In the last step, do not write the observation out of bounds.
if self.current_step < self.max_steps:
self.additional_info["observation_input_requests"][agent][self.current_step] = obs[agent]["input_requests"]
self.additional_info["observation_queue_capacity"][agent][self.current_step] = obs[agent]["queue_capacity"]
if self.current_step == 0:
# After reset() there is no action, reward and info_work.
continue
# These values refer to the previous step, so there is -1.
self.additional_info["action_local"][agent][self.current_step-1] = action[agent][0]
self.additional_info["action_reject"][agent][self.current_step-1] = action[agent][1]
self.additional_info["excess_local"][agent][self.current_step-1] = info_work[agent]["local_excess"]
self.additional_info["reward"][agent][self.current_step-1] = rewards[agent]
# Since there is no forwarding, pre/post is indifferent.
self.additional_info["queue_status_pre_forward"][agent][self.current_step-1] = info_work[agent]["queue_status"]
self.additional_info["queue_status_post_forward"][agent][self.current_step-1] = info_work[agent]["queue_status"]
class DFaaS_ASYM(MultiAgentEnv):
# String representing the type of this environment.
type = "ASYM"
# Keys contained in the additional_info dictionary.
info_keys = {"observation_input_requests": np.int32,
"observation_queue_capacity": np.int32,
"action_local": np.int32,
"action_forward": np.int32,
"action_reject": np.int32,
"excess_local": np.int32,
"excess_forward_reject": np.int32,
"queue_status_pre_forward": np.int32,
"queue_status_post_forward": np.int32,
"reward": np.float32}
def __init__(self, config={}):
# Number and IDs of the agents in the DFaaS network.
self.agents = 2
self.agent_ids = ["node_0", "node_1"]
# String representing the type of this environment.
self.suffix = "ASYM"
# This attribute is required by Ray and must be a set. I use the list
# version instead in this environment.
self._agent_ids = set(self.agent_ids)
# It is the possible max and min value for the reward returned by the
# step() call.
self.reward_range = (.0, 1.)
# The size of each agent's local queue. The queue can be filled with
# requests to be processed locally.
self.queue_capacity_max = {
"node_0": config.get("queue_capacity_max_node_0", 100),
"node_1": config.get("queue_capacity_max_node_1", 100),
}
# Provide full (preferred format) observation- and action-spaces as
# Dicts mapping agent IDs to the individual agents' spaces.
self._action_space_in_preferred_format = True
self.action_space = gym.spaces.Dict({
# Distribution of how many requests are processed locally, forwarded
# and rejected.
"node_0": Simplex(shape=(3,)),
# Distribution of how many requests are processed locally and
# rejected.
"node_1": Simplex(shape=(2,))
})
self._obs_space_in_preferred_format = True
self.observation_space = gym.spaces.Dict({
"node_0": gym.spaces.Dict({
# Number of input requests to process for a single step.
"input_requests": gym.spaces.Box(low=0, high=150, dtype=np.int32),
# Queue capacity (currently a constant).
"queue_capacity": gym.spaces.Box(low=0, high=self.queue_capacity_max["node_0"], dtype=np.int32),
# Forwarded requests in the previous step.
"last_forward_requests": gym.spaces.Box(low=0, high=150, dtype=np.int32),
# Forwarded but rejected requests in the previous step. Note
# that last_forward_rejects <= last_forward_requests.
"last_forward_rejects": gym.spaces.Box(low=0, high=150, dtype=np.int32)
}),
"node_1": gym.spaces.Dict({
# Number of input requests to process for a single step.
"input_requests": gym.spaces.Box(low=0, high=150, dtype=np.int32),
# Queue capacity (currently a constant).
"queue_capacity": gym.spaces.Box(low=0, high=self.queue_capacity_max["node_1"], dtype=np.int32)
})
})
# Number of steps in the environment. The default is one step for every
# 5 minutes of a 24-hour day.
self.max_steps = config.get("max_steps", 288)
# Type of input requests.
self.input_requests_type = config.get("input_requests_type", "synthetic-sinusoidal")
match self.input_requests_type:
case "synthetic":
print("WARN: 'synthetic' type deprecated, use 'synthetic-sinusoidal'")
pass
case "synthetic-sinusoidal":
pass
case "synthetic-normal":
pass
case "real":
assert self.max_steps == 288, f"With {self.input_requests_type = } only 288 max_steps are supported"
case _:
assert False, f"Unsupported {self.input_requests_type = }"
# Is the env created for evaluation only? If so, the input requests may
# differ from the training ones.
self.evaluation = config.get("evaluation", False)
super().__init__()
def get_config(self):
"""Returns a dictionary with the current configuration of the
environment."""
config = {}
config["queue_capacity_max_node_0"] = self.queue_capacity_max["node_0"]
config["queue_capacity_max_node_1"] = self.queue_capacity_max["node_1"]
config["max_steps"] = self.max_steps
config["input_requests_type"] = self.input_requests_type
return config
def reset(self, *, seed=None, options=None):
# Current step.
self.current_step = 0
# If seed is given, overwrite the master seed. Ray will give the seed in
# reset() only when it creates the environment for each rollout worker
# (and local worker). Each worker has a specific seed.
if seed is not None:
# The master seed for the RNG. This is used in each episode (each
# "reset()") to create a new RNG that will be used for generating
# input requests.
#
# Using the master seed make sure to generate a reproducible
# sequence of seeds.
self.master_seed = seed
self.master_rng = np.random.default_rng(seed=self.master_seed)
# Seed used for this episode.
if isinstance(options, dict) and "override_seed" in options:
# By default, the seed is generated. But can be overrided (usually
# on manual calls, not from Ray).
self.seed = options["override_seed"]
else:
iinfo = np.iinfo(np.uint32)
self.seed = self.master_rng.integers(0, high=iinfo.max, size=1)[0]
# Create the RNG used to generate input requests.
self.rng = np.random.default_rng(seed=self.seed)
self.np_random = self.rng # Required by the Gymnasium API
# Generate all input requests for the environment.
limits = {}
for agent in self.agent_ids:
limits[agent] = {
"min": self.observation_space[agent]["input_requests"].low.item(),
"max": self.observation_space[agent]["input_requests"].high.item()
}
if self.input_requests_type == "synthetic-sinusoidal" or self.input_requests_type == "synthetic":
self.input_requests = _synthetic_sinusoidal_input_requests(self.max_steps,
self.agent_ids,
limits,
self.rng)
elif self.input_requests_type == "syntethic-normal":
self.input_requests = _synthetic_normal_input_requests(self.max_steps,
self.agent_ids,
limits,
self.rng)
else: # "real"
retval = _real_input_requests(self.max_steps, self.agent_ids,
limits, self.rng, self.evaluation)
self.input_requests = retval[0]
# Special attribute, not returned in the observation: contains the
# hashes of the selected input requests. It is used by the
# callbacks.
self.input_requests_hashes = retval[1]
# Queue state for each agent (number of requests to process locally).
# The queues start empty (max capacity) and can be full.
self.queue = {agent: 0 for agent in self.agent_ids}
self.last_info = None # Required by _build_observation().
obs = self._build_observation()
# For each reset() and step() call, the info dictionary is stored in an
# attribute and is not returned. The caller can access this attribute
# directly (usually at the end of the episode).
#
# To update the dictionary, a private function is called at the end of
# reset() and call().
self.additional_info = None
self._additional_info(obs)
return obs, {}
def step(self, action_dict):
# Action for node_0.
input_requests_0 = self.input_requests["node_0"][self.current_step]
# Convert the action distribution (a distribution of probabilities) into
# the number of requests to locally process, to forward and to reject.
action_0 = _convert_distribution_fw(input_requests_0, action_dict["node_0"])
# Action for node_1.
input_requests_1 = self.input_requests["node_1"][self.current_step]
# Convert the action distribution (a distribution of probabilities) into
# the number of requests to locally process and reject.
action_1 = _convert_distribution_no_fw(input_requests_1, action_dict["node_1"])
# We have the actions, now update the environment state.
info_work = self._manage_workload(action_0, action_1)
# Calculate the reward for both agents.
rewards = {}
rewards["node_0"] = _reward_fw(action_0,
(info_work["node_0"]["local_excess"], info_work["node_0"]["forward_rejects"]),
(self.queue["node_0"], self.queue_capacity_max["node_0"]))
rewards["node_1"] = _reward_no_fw(action_1,
(info_work["node_1"]["local_excess"],),
(info_work["node_1"]["queue_status_pre_forward"], self.queue_capacity_max["node_1"]))
# Make sure the reward is of type float.
for agent in self.agent_ids:
rewards[agent] = float(rewards[agent])
# Required by _build_observation().
self.last_info = {
"action": {"node_0": action_0, "node_1": action_1},
"rewards": rewards,
"workload": info_work
}
# Go to the next step.
self.current_step += 1
# Free the queues (for now...).
self.queue = {agent: 0 for agent in self.agent_ids}
# Each key in the terminated dictionary indicates whether an individual
# agent has terminated. There is a special key "__all__" which is true
# only if all agents have terminated.
terminated = {agent: False for agent in self.agent_ids}
if self.current_step == self.max_steps:
# We are past the last step: nothing more to do.
terminated = {agent: True for agent in self.agent_ids}
terminated["__all__"] = all(terminated.values())
# Truncated is always set to False because it is not used.
truncated = {agent: False for agent in self.agent_ids}
truncated["__all__"] = False
if self.current_step < self.max_steps:
obs = self._build_observation()
else:
# Return a dummy observation because this is the last step.
obs = self.observation_space_sample()
# Update the additional_info dictionary.
self._additional_info(obs, self.last_info["action"], rewards, info_work)
return obs, rewards, terminated, truncated, {}
def _build_observation(self):
"""Builds and returns the observation for the current step."""
assert self.current_step < self.max_steps
# Initialize the observation dictionary.
obs = {agent: {} for agent in self.agent_ids}
# Set common observation values for the agents.
for agent in self.agent_ids:
# The queue capacity is always a fixed value for now.
obs[agent]["queue_capacity"] = np.array([self.queue_capacity_max[agent]], dtype=np.int32)
input_requests = self.input_requests[agent][self.current_step]
obs[agent]["input_requests"] = np.array([input_requests], dtype=np.int32)
# Set the ratio of forwarded but rejected requests.
if self.last_info is None:
last_forward_reqs = last_forward_rejects = 0
else:
last_forward_reqs = self.last_info["action"]["node_0"][1]
last_forward_rejects = self.last_info["workload"]["node_0"]["forward_rejects"]
obs["node_0"]["last_forward_requests"] = np.array([last_forward_reqs], dtype=np.int32)
obs["node_0"]["last_forward_rejects"] = np.array([last_forward_rejects], dtype=np.int32)
return obs
def _calculate_reward_1(self, action, excess):
"""Returns the reward for the agent "node_1" for the current step.
This old reward function is DEPRECATED.
The reward is based on:
- The action, a 2-length tuple containing the number of requests to
process locally and to reject.
- The excess, a 1-length tuple containing the local requests that
exceed the queue capacity.
This reward function assumes that the queue starts empty at each
step."""
assert len(action) == 2, "Expected (local, reject)"
assert len(excess) == 1, "Expected (local_excess)"
reqs_total = sum(action)
reqs_local, reqs_reject = action
local_excess = excess[0]
# The agent (policy) tried to be sneaky, but it is not possible to
# locally process more requests than the internal limit for each step.
# This behavior must be discouraged by penalizing the reward, but not as
# much as by rejecting too many requests (the .5 factor).
if local_excess > 0:
return 1 - (local_excess / self.queue_capacity_max["node_1"]) * .5
# If there are more input requests than available slots in the agent
# queue, the optimal strategy should be to fill the queue and then
# reject the other requests.
if reqs_total > self.queue_capacity_max["node_1"]:
# The reward penalises the agent if the action doesn't maximise the
# request process locally.
if reqs_local < self.queue_capacity_max["node_1"]:
# The new value is the number of rejected requests that will be
# considered a penalty for the reward. Note that some rejections
# are inevitable and will not be penalized, only those that can
# be processed locally but the agent didn't.
reqs_reject = self.queue_capacity_max["node_1"] - reqs_local
reqs_total = self.queue_capacity_max["node_1"]
else:
reqs_reject = 0
# The reward is a range from 0 to 1. It decreases as the number of
# unnecessary rejected requests increases.
return 1 - reqs_reject / reqs_total
def _calculate_reward_0(self, action, excess, forward_capacity):
"""Returns the reward for the agent "node_0" for the current step.
This old reward function is DEPRECATED.
The reward is based on:
- The action, a 3-length tuple containing the number of requests to
process locally, to forward and to reject.
- The excess, a 3-length tuple containing the local requests that
exceed the queue capacity, the forwarded requests that exceed the
forwarding capacity, and the forwarded requests that were rejected
by the other agent.
This reward function assumes that the queue starts empty at each step.
"""
assert len(action) == 3, "Expected (local, forward, reject)"
assert len(excess) == 3, "Expected (local_excess, forward_excess, forward_reject)"
reqs_total = sum(action)
reqs_local, reqs_forward, reqs_reject = action
local_excess, forward_excess, forward_reject = excess
assert local_excess <= reqs_local
assert forward_excess <= reqs_forward
assert forward_reject <= reqs_forward
assert forward_capacity >= 0
reward = 1
# The agent (policy) tried to be sneaky, but it is not possible to
# locally process more requests than the internal limit for each step.
# This behavior must be discouraged by penalizing the reward, but not as
# much as by rejecting too many requests (the .5 factor).
if local_excess > 0:
reward -= (local_excess / self.queue_capacity_max["node_0"]) * .6
# The same also for forwarding.
if forward_excess > 0:
if forward_capacity > 0:
reward -= (forward_excess / forward_capacity) * .3
else:
reward -= .3
if forward_capacity > 0:
reward -= (forward_reject / forward_capacity) * .4
# If there are more requests than the node can handle locally, the
# optimal strategy should be to process all possible requests locally
# and forward or reject the extra ones.
if reqs_total > self.queue_capacity_max["node_0"]:
# The reward penalises the agent if the action doesn't maximise the
# request process locally.
if reqs_local < self.queue_capacity_max["node_0"]:
# The new value is the number of rejected requests that will be
# considered a penalty for the reward. Note that some rejections
# are inevitable and will not be penalized, only those that can
# be processed locally but the agent didn't.
reqs_reject = self.queue_capacity_max["node_0"] - reqs_local - reqs_forward
reqs_reject = np.clip(reqs_reject, a_min=0, a_max=None)
reqs_total = self.queue_capacity_max["node_0"] + reqs_forward
elif reqs_forward < forward_capacity:
reqs_reject = reqs_reject - (forward_capacity - reqs_forward)
reqs_reject = np.clip(reqs_reject, a_min=0, a_max=None)
reqs_total = self.queue_capacity_max["node_0"] + reqs_forward
else:
reqs_reject = 0
# The reward is a range from 0 to 1. It decreases as the number of
# unnecessary rejected requests increases.
reward -= (reqs_reject / reqs_total) * 2
reward = np.clip(reward, .0, 1.)
return reward
@staticmethod
def _calculate_reward_0_v2(action, excess, queue):
"""Returns the reward for agent "node_0" for the current step.
The reward is based on:
- The action, a 3-length tuple containing the number of requests to
process locally, to forward, and to reject.
- The excess, a 2-length tuple containing the local requests that
exceed the queue capacity and the forwarded requests that were
rejected by the other agent.
- The queue, a 2-length tuple containing the status of the queue (0
is empty) and the maximum capacity of the queue.
The reward returned is in the range [0, 1]."""
assert len(action) == 3, "Expected (local, forward, reject)"
assert len(excess) == 2, "Expected (local_excess, forward_reject)"
assert len(queue) == 2, "Expected (queue_status, queue_max_capacity)"
reqs_total = sum(action)
reqs_local, reqs_forward, reqs_reject = action
local_excess, forward_reject = excess
queue_status, queue_max = queue
if reqs_total == 0:
return 1.
free_slots = queue_max - queue_status
assert free_slots >= 0
# Calculate the number of excess forward requests.
#
# The forwarded and rejected requests must be excluded from the count
# because they are considered separately.
reqs_forward -= forward_reject
if free_slots > reqs_forward:
forward_excess = reqs_forward
else: # reqs_forward >= free_slots
valid_forward = reqs_forward - free_slots
assert valid_forward >= 0
forward_excess = reqs_forward - valid_forward
# Calculate the number of excess reject requests.
free_slots = free_slots - forward_excess
assert free_slots >= 0
if free_slots > reqs_reject:
valid_reject = 0
reject_excess = reqs_reject
else: # reqs_reject >= free_slots
valid_reject = reqs_reject - free_slots
assert valid_reject >= 0
reject_excess = reqs_reject - valid_reject
# Calculate the number of rejected requests that could have been
# forwarded.
if forward_reject == 0 and valid_reject > 0:
# Assume that all rejected requests could have been forwarded
# because no forwarded requests were rejected.
reject_excess += valid_reject
valid_reject = 0
assert local_excess >= 0 \
and forward_reject >= 0 \
and forward_excess >= 0 \
and reject_excess >= 0
wrong_reqs = local_excess + forward_reject + forward_excess + reject_excess
assert wrong_reqs <= reqs_total, f"({local_excess = } + {forward_reject = } + {forward_excess = } + {reject_excess = }) <= {reqs_total}"
return 1 - (wrong_reqs / reqs_total)
@staticmethod
def _convert_distribution_0(input_requests, action_dist):
"""Converts the given action distribution (e.g. [.7, .2, .1]) into the
absolute number of requests to process locally, to forward and to
reject. Returns the result as a tuple.
This function is only for node_0 agent."""
assert len(action_dist) == 3, "Expected (local, forward, reject)"
# Extract the three actions from the action distribution
prob_local, prob_forwarded, prob_rejected = action_dist
# Get the corresponding number of requests for each action. Note: the
# number of requests is a discrete number, so there is a fraction of the
# action probabilities that is left out of the calculation.
actions = [
int(prob_local * input_requests), # local requests
int(prob_forwarded * input_requests), # forwarded requests
int(prob_rejected * input_requests)] # rejected requests
processed_requests = sum(actions)
# There is a fraction of unprocessed input requests. We need to fix this
# problem by assigning the remaining requests to the higher fraction for
# the three action probabilities, because that action is the one that
# loses the most.
if processed_requests < input_requests:
# Extract the fraction for each action probability.
fractions = [prob_local * input_requests - actions[0],
prob_forwarded * input_requests - actions[1],
prob_rejected * input_requests - actions[2]]
# Get the highest fraction index and and assign remaining requests
# to that action.
max_fraction_index = np.argmax(fractions)
actions[max_fraction_index] += input_requests - processed_requests
assert sum(actions) == input_requests
return tuple(actions)