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parameters.py
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parameters.py
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'''
Parameters of the simulation
'''
import numpy as np
SAVE_FIGS = False
ONE_FIGURE = True
LOAD_SAVED_PARAMETERS = True
SAVE_PARAMETERS = False
SAVE_RESULTS = True
CONSTANT_PRICING = False
CONSTANT_OFFLOADING = False
def set_parameters(case):
'''
Sets the parameters used in the simulation
Parameters
----------
case: dictionary
Dictionary containing infromation about whether the user and the servers
are homogeneous or heterogeneous
Returns
----------
S: int
Number of servers
U: int
Number of users
e1: float
Error for user offloading convergence
e2: float
Error for server pricing convergence
k: int
parameter of the user's satisfaction function
l: int
parameter of the user's satisfaction function
a: 1-D array
parameter that reflects users' dynamic behavior to spen more money
in order to buy computing support from the MEC servers
b_min: int
Minimum number of bits that the user is willing to offload
Same for all users
b_max: int
Maximum number of bits that the user is willing to offload
Same for all users
c: 1-D array
parameter that shows the server's computing cost
fs: 1-D array
parameter that shows the server's discount
price_min: int
Minimum vlaue that the server can set his price
learning_rate: float
parameter indicating the learning rate of the server selection learning
mechanism
'''
S = 5
U = 100
e1 = 1e-02
e2 = 1e-02
k = 100
l = 1000
# User parameters
if case["users"] == "homo":
# a = 1*1e3 * np.ones(U) + 0.5e4
a = 1*1e2 * np.ones(U) + 0.5e3
if case["users"] == "hetero":
a = 1e3 + np.random.random(U)*1e4
b_min = 0
b_max = 1000
# Server parameters
if case["servers"] == "homo":
c = 0.2 * np.ones(S)
fs = 0.025 * np.ones(S)
if case["servers"] == "hetero":
c = np.array([0.12, 0.14, 0.2, 0.17, 0.13])
fs = np.array([0.05, 0.04, 0.02, 0.03, 0.05])
# c = 0.2 + np.random.random(S)
# fs = 0.025 + np.random.random(S) * 0.1
if case["servers"] == "one-dominant":
c = np.array([0.12, 0.2, 0.2, 0.2, 0.2])
fs = np.array([0.05, 0.02, 0.02, 0.02, 0.02])
if case["servers"] == "two-dominant":
c = np.array([0.12, 0.12, 0.2, 0.2, 0.2])
fs = np.array([0.05, 0.05, 0.02, 0.02, 0.02])
# c = np.array([0.1, 0.12, 0.5, 0.5, 0.5])
# fs = np.array([0.1, 0.09, 0.01, 0.01, 0.01])
price_min = 0.5
learning_rate = 0.2
return locals()