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Set_parameters_DAG_11.m
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Set_parameters_DAG_11.m
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% Setting all the global variables for DAG #11
%
% DAG #11 describes a CyberShake workflow with V = 25 tasks.
%
% ***************************************************
% * Package: VirtFogSim *
% * Author: Enzo Baccarelli and Michele Scarpiniti *
% * Date: January, 2019 *
% * Version: 4.0 *
% ***************************************************
%
fprintf('..DAG #11..');
%--------------------------------------------------------------------------
% Begin the declaration of the 61 global variables
global V
global s
global A
global Da
global n_M
global n_F
global n_C
global NF_WiFi
global NF_CELL
global NF_WD
global f_MAX_M
global f_MAX_F
global f_MAX_C
global R_MAX_U
global R_MAX_D
global B_MAX_U
global B_MAX_D
global TH_MIN_0
global Teta_M
global Teta_F
global Teta_C
global P_IDLE_CPU_M
global P_IDLE_CPU_F
global P_IDLE_CPU_C
global nc_M
global nc_F
global nc_C
global gamma_M
global gamma_F
global gamma_C
global K_M
global K_F
global K_C
global r_M
global r_F
global r_C
global P_IDLE_ETH
global P_IDLE_WiFi_M
global P_IDLE_CELL_M
global zeta_TX_WiFi
global zeta_RX_WiFi
global zeta_TX_CELL
global zeta_RX_CELL
global eta
global RTT_WiFi
global RTT_CELL
global RTT_WD
global MSS
global Prob_LOSS
global chi_TX_WiFi
global chi_RX_WiFi
global chi_TX_CELL
global chi_RX_CELL
global I_MAX
global a_max
global no_HOP
global P_HOP
global PS
global CF
global G_MAX
global MN
global iteration_number
global jump1_WiFi
global jump1_CELL
global jump2_WiFi
global jump2_CELL
global a_max_vec_FT
% End of the declaration of 61 global variables
%-----------------------------------------------------------------------
%--------------------------------------------------------------------------
% BEGIN THE SETUP OF 61 INPUT PARAMETERS: TO BE PERFORMED BY THE USER
%
V = 25; % Positive scalar integer value. It is the number of tasks of the
% application DAG. It must be: V >= 3.
%
% Constants used to scale the workload
c1 = 1e+3;
c2 = 1e+3;
%
k1 = 2.0;
% k1 = 1;
% k1 = 0.5;
k2 = 1;
%
s = k1*c1*[26,50,350,30*ones(1,4),130*ones(1,4),40*ones(1,4),4, 40*ones(1,4), 30*ones(1,2), 92*ones(1,2),26]; %DAG_A (CyberShake workflow)
%
% (1xV) row vector of the sizes of the tasks of the application DAG.
% Each entry is a non-negative real number and it is measured in (bit).
%
A = [0, 1, 1, zeros(1,22); %DAG_A (CyberShake workflow)
0, 0, 0, ones(1,4), zeros(1,18);
zeros(1,7), ones(1,4), zeros(1,14);
zeros(1,11), 1, zeros(1,3), 1, zeros(1,9);
zeros(1,12), 1, zeros(1,2), 1, zeros(1,9);
zeros(1,13), 1, 0, 1, zeros(1,9);
zeros(1,14), 1, 1, zeros(1,9);
zeros(1,15), 1, 1, zeros(1,8);
zeros(1,18), 1, zeros(1,6);
zeros(1,15), 1, 0, 1, zeros(1,7);
zeros(1,15), 1, 0, 0, 1, zeros(1,6);
zeros(1,15), 1, 0, 0, 0, 1, zeros(1,5);
zeros(1,20), 1, zeros(1,4);
zeros(1,20), 1, zeros(1,4);
zeros(1,21), 1, zeros(1,3);
zeros(1,24), 1;
zeros(1,22), 1, 0, 0;
zeros(1,22), 1, 0, 0;
zeros(1,23), 1, 0;
zeros(1,23), 1, 0;
zeros(1,24), 1;
zeros(1,24), 1;
zeros(1,24), 1;
zeros(1,24), 1;
zeros(1,25)];
% (VxV) binary (i.e,(0,1)) adjacency matrix of the application DAG.
% a(i,j)=1 (resp., a(i,j)=0) means that there is (resp., there is not)
% a DIRECTED edge FROM node i TO node j in the application DAG.
%
Da = k2*c2*[0, 10, 110, zeros(1,22); %DAG_A (CyberShake workflow)
0, 0, 0, 50*ones(1,4), zeros(1,18);
zeros(1,7), 135.5*ones(1,4), zeros(1,14);
zeros(1,11), 40, zeros(1,3), 2, zeros(1,9);
zeros(1,12), 40, zeros(1,2), 2, zeros(1,9);
zeros(1,13), 40, 0, 2, zeros(1,9);
zeros(1,14), 40, 2, zeros(1,9);
zeros(1,15), 2, 40, zeros(1,8);
zeros(1,15), 2, 0, 40, zeros(1,7);
zeros(1,15), 2, 0, 0, 40, zeros(1,6);
zeros(1,15), 2, 0, 0, 0, 40, zeros(1,5);
zeros(1,20), 30, zeros(1,4);
zeros(1,20), 30, zeros(1,4);
zeros(1,21), 30, zeros(1,3);
zeros(1,21), 30, zeros(1,3);
zeros(1,24), 128;
zeros(1,22), 30, 0, 0;
zeros(1,22), 30, 0, 0;
zeros(1,23), 30, 0;
zeros(1,23), 30, 0;
zeros(1,24), 5;
zeros(1,24), 5;
zeros(1,24), 42;
zeros(1,24), 42;
zeros(1,25)];
% (VxV) non-negative real-valued matrix of the labels of the edges
% of the application DAG. Specifically, d(i,j) is the label of the DIRECTED
% edge FROM node i TO node j in the application DAG. It represents the
% volume of the data that node i GIVES in input TO node j.
% Each entry of the Da matrix is measured in (bit).
% If there is NOT a DIRECTED edge FROM node i TO node j in the
% application DAG, THEN d(i,j) = 0 (bit).
%
n_M = 1; % It is a scalar positive integer, i.e., n_M >= 1. It is the
% number of the (virtual) computing cores that equip
% the Mobile device.
%
n_F = 4; % It is a scalar positive integer,i.e., n_F >= 1. It is the number
% of the (virtual) computing cores that equip the device clone
% at the Fog node.
%
n_C = 12; % It is a scalar positive integer, i.e., n_C >= 1. It is the number
% of (virtual) computing cores that equip the device clone
% at the Cloud node. Typically, 1 <= n_M <= n_F <= n_C.
%
%
NF_WiFi = 1.1; % Scalar non-negative real-valued parameter, i.e.,
% NF_WiFi >= 0. It represents the average per-connection
% number of failures of the WiFi-supported uni-directional
% TCP/IP connections: Mobile -> Fog and Fog -> Mobile.
%
NF_CELL = 0.1; % Scalar non-negative real-valued parameter, i.e.,
% NF_CELL >= 0. It represents the average per-connection
% number of failures of the cellular uni-directional
% TCP/IP connections: Mobile -> Cloud and Cloud -> Mobile.
%
NF_WD = 0.01; % Scalar non-negative real-valued parameter, i.e.,
% NF_WD >= 0. It represents the average per-connection number
% of failures of the fixed uni-directional TCP/IP backbone
% connections: Cloud -> Fog and Fog -> Cloud.
% Typically, 0 <= NF_WD < NF_CELL < NF_WiFi.
%
f_MAX_M = 12e+6; % Per-core maximum computing frequency at the Mobile device.
% It is measured in (bit/sec).
%
f_MAX_F = 12e+6; % Per-core maximum computing frequency at the Fog clone.
% It is measured in (bit/sec).
% Typically, f_MAX_F >= 10 x f_MAX_M.
%
f_MAX_C = 12e+6; % Per-core maximum computing frequency at the Cloud clone.
% It is measured in (bit/sec).
% Typically, f_MAX_C >= 100 x f_MAX_M.
%
R_MAX_U = 8.0e+6; % Maximum bit rate of the WiFi-based unidirectional
% TCP/IP connection: Mobile -> Fog.
% It is measured in (bit/sec).
%
R_MAX_D = 9.0e+6; % Maximum bit rate of the WiFi-based unidirectional
% TCP/IP connection: Fog -> Mobile.
% It is measured in (bit/sec).
%
B_MAX_U = 6.5e+6; % Maximum bit rate of the 3G/4G cellular unidirectional
% TCP/IP connection: Mobile -> Cloud.
% It is measured in (bit/sec).
%
B_MAX_D = 7.0e+6; % Maximum bit rate of the 3G/4G cellular unidirectional
% TCP/IP connection: Cloud -> Mobile.
% It is measured in (bit/sec).
%
% Typically,
% B_MAX_U(3G) = 2 x 10^6 (bit/sec)
% R_MAX_U (IEEE802.11b) = 8 x 10^6 (bit/sec)
% B_MAX_D (3G) = 2.5 x 10^6 (bit/sec)
% R_MAX_D (IEEE802.11b) = 9 x 10^6 (bit/sec)
% B_MAX_U (4G) = 9.4 x 10^6 (bit/sec)
% B_MAX_D (4G) = 20.8 x 10^6 (bit/sec).
% Fixing R_MAX_U, R_MAX_D, B_MAX_U, and B_MAX_D is equivalent to fix
% the sizes of the maximum congestion windows of the corresponding
% TCP-NewReno connections.
%
TH_MIN_0 = 1/0.3; %Scalar non-negative real parameter, i.e., TH_MIN_0 >= 0.
% It is the minimum required application throughput and it
% is measured in processed application DAG per second,
% i.e.,(app/sec).
% Typically, TH_MIN_0 >= 0.1 (app/sec).
% TH_MIN_0 = 1;
% TH_MIN_0 = 1/2;
%
Teta_M = 1; % Binary 0-1 parameter.
%
Teta_F = 1; % Binary 0-1 parameter.
%
Teta_C = 1; % Binary 0-1 parameter.
% Actual values of Teta_M, Teta_F and Teta_C are dictated by the applied
% application service model. Typically, Teta_M = Teta_F = Teta_C = 1.
%
%
P_IDLE_CPU_M = 1.2; % Power consumed by the physical CPU at the Mobile
% device in the idle state. It is measured in (Watt).
%
P_IDLE_CPU_F = 220; % Power consumed by a single physical server in the
% idle state at the Fog node. It is measured in (Watt).
%
P_IDLE_CPU_C = 440; %Power consumed by a single physical server in the
% idle state at the Cloud node.
% It is measured in (Watt).
% Typically, 0 < P_IDLE_CPU_M << P_IDLE_CPU_F <= P_IDLE_CPU_C.
%
%
nc_M = 1; % Dimension-less scalar positive integer parameter,
% i.e., nc_M >= 1. It is the average number of containers that
% SIMULTANEOUSLY run atop the CPU at the Mobile device.
%
nc_F = 16; % Dimension-less scalar positive integer parameter,
% i.e., nc_F >= 1. It is the average number of containers that
% SIMULTANEOUSLY run atop a SINGLE physical server at the
% Fog node.
%
nc_C = 24; % Dimension-less scalar positive integer parameter,
% i.e., nc_C >= 1. It is the average number of containers that
% SIMULTANEOUSLY run atop a SINGLE physical server at the
% Cloud node.
% Typically, nc_M = 1, nc_F = 60 x nc_M, and nc_C = 90 x nc_M.
%
gamma_M = 3.2;% Dimension-less positive scalar exponent of the dynamic power
% consumption of the CPU at the Mobile device.
% Typically, gamma_M = 3.
%
gamma_F = 3.1; % Dimension-less positive scalar exponent of the dynamic power
% consumption of a single physical server at the Fog node.
% Typically, gamma_F = 3.
%
gamma_C = 3.0; % Dimension-less positive scalar exponent of the dynamic power
% consumption of a single physical server at the Cloud node.
% Typically, gamma_C = 3.
%
K_M = 7.5e-21; % Positive scalar parameter. It is the profile of the dynamic
% power consumption of the CPU at the Mobile device.
% It is measured in: (Watt)/((bit/sec)^gamma_M).
%
K_F = 3.0*3.26e-20; % Positive scalar parameter. It is the profile of the dynamic
% power consumption of a single physical server at
% the Fog node. It is measured in:(Watt)/((bit/sec)^gamma_F).
%
K_C = 3.5*3.26e-20;% Positive scalar parameter. It is the profile of the dynamic
% power consumption of a single physical server at the
% Cloud node. It is measured in: (Watt)/((bit/sec)^gamma_C).
%
% Typically, K_M = 0.5e-9 (Watt/(bit/sec)^3), K_F = 0.5 x K_M,
% and K_C = 0.02 x K_M.
%
r_M = 0.0; % Dimension-less scalar fraction of the overall
% computing power shared by the computing cores
% at the Mobile device. 0 <= r_M < 1.
%
r_F = 0.2; % Dimension-less scalar fraction of the overall
% computing power shared by the computing cores of a single
% physical server at the Fog node. 0 <= r_F < 1.
%
r_C = 0.1; % Dimension-less scalar fraction of the overall computing power
% shared by the computing cores of a single physical server
% at the Cloud node. 0 <= r_C < 1.
% Typically, r_M = 0, r_F = 0.2, and r_C = 0.4.
%
%
P_IDLE_ETH = 1.0e-4; % Power consumed in the idle state
% by each physical Ethernet Network Interface Card (NIC)
% that equips the Fog and Cloud nodes.
% It is measured in (Watt).
%
P_IDLE_WiFi_M = 1.3; % Power consumed in the idle state by each physical
% WiFi NIC that equips the Mobile device
% and the corresponding Fog clone.
% It is measured in (Watt).
%
P_IDLE_CELL_M = 0.818; % Power consumed in the idle state by each physical
% cellular 3G/4G NIC that equips the Mobile device
% and the corresponding Cloud clone.
% It is measured in (Watt).
%
% Typically,
% 0 < P_IDLE_ETH << P_IDLE_CELL_M << P_IDLE_WiFi_M,
% with:
% P_IDLE_ETH = 0.0001 (Watt), P_IDLE_CELL_M (3G) = 0.133 (Watt),
% P_IDLE_CELL_M (4G) = 0.818 (Watt), and P_IDLE_WiFi_M = 1.3 (Watt).
%
zeta_TX_WiFi = 2.4; % Dimension-less positive scalar exponent
% of the dynamic power consumption of the WiFi NIC
% in the transmit mode.
%
zeta_RX_WiFi = 2.2; % Dimension-less positive scalar exponent
% of the dynamic power consumption of the WiFi NIC
% in the receive mode.
%
zeta_TX_CELL = 2.45; % Dimension-less positive scalar exponent
% of the dynamic power consumption of the cellular
% 3G/4G NIC in the transmit mode.
%
zeta_RX_CELL = 2.34; % Dimension-less positive scalar exponent
% of the dynamic power consumption of the cellular
% 3G/4G NIC in the receive mode.
%
eta = 0.6; % Dimensionless scalar positive exponent of the
% Round-Trip-Times (RTTs) of the TCP/IP WiFi and Cellular
% connections. Typically, 0.5 <= eta <= 0.7.
%
RTT_WiFi = 10e-3; % Average RTT of the TCP/IP WiFi connections.
% It is measured in (sec).
% Typically, 0.005 (sec) <= RTT_WiFi <= 0.15 (sec).
%
RTT_CELL = 10e-2; % Average RTT of the TCP/IP cellular connections.
% It is measured in (sec).
% Typically, 0.03(sec) <= RTT_CELL <= 0.05(sec).
%
RTT_WD = 10e-1; % Average RTT of the (possibly, multi-hop) TCP/IP
% Cloud - Fog Internet backbone connection.
% It is measured in (sec).
% Typically, 0.2(sec) <= RTT_WiFi <= 0.33(sec).
%
%
MSS = 12e+3; % Maximum size of a TCP segment.
% It is measured in (bit). Typically,MSS = 12000 (bit).
%
%
Prob_LOSS = 1.56e-5; % Average segment loss probability of the TCP/IP
% Cloud - Fog connection.
% Typically, 10^(-8) <= Prob_LOSS <= 10^(-7).
%
chi_TX_WiFi = 5*1.0e-14; % Power profile of the WiFi NIC in the transmit mode.
% It is measured in:
% (Watt)/(((bit/sec)^(zeta_TX_WiFi)) x ((sec)^(eta))).
%
chi_RX_WiFi = 5*2.8e-15; % Power profile of the WiFi NIC in the receive mode.
% It is measured in:
% (Watt)/(((bit/sec)^(zeta_RX_WiFi)) x ((sec)^(eta))).
%
chi_TX_CELL = 6*3.85e-14;% Power profile of the cellular NIC in the
% transmit mode. It is measured in:
% (Watt)/(((bit/sec)^(zeta_TX_CELL)) x ((sec)^(eta))).
%
chi_RX_CELL = 6*1.35e-15;% Power profile of the cellular NIC in the
% transmit mode. It is measured in:
% (Watt)/(((bit/sec)^(zeta_RX_CELL)) x ((sec)^(eta))).
%
I_MAX = 700; % Positive scalar integer parameter, i.e., I_MAX >= 1.
% It is the maximum number of the primal-dual iterations
% performed by the RAP function.
% Typically, 200 <= I_MAX <= 700.
%
a_max = 9.9e-8; % Dimension-less positive scalar real parameter,
% i.e., a_max > 0. It is the speed-factor of the
% gradient-based iterations performed by the RAP function.
% It must be tuned by trials, in order to speed-up the
% convergence rate of the RAP iterations, without causing
% oscillations at the convergence.
% Typically, a_max is of the order of: 10^-7 - 10^-8.
%
%
no_HOP = 4; % Dimension-less positive integer scalar parameter, i.e.,
% no_HOP >= 1. It is the number of hops of the
% Cloud <-> Fog bidirectional
% Internet backbone connection. Typically, no_HOP >= 2.
%
P_HOP = 5.7e-1; %5.7e-1 GOOD; % Average power consumed by a SINGLE hop of the
% Cloud <-> Fog bidirectional Internet backbone connection.
% It is measured in (Watt).
% Typically, P_HOP > 2.5 x 10^(-1) (Watt).
%
PS = 150; % Dimension-less positive even integer scalar parameter.
% It must be EVEN and NOT BELOW 4, i.e., PS is an
% even integer and PS >= 4.
% It is the size of the population processed by
% all the called genetic functions.
%
CF = 0.5; % Fraction of the population size PS that is subject
% to cross over by all the called genetic functions.
% It must be: 0 < CF < 1.
%
G_MAX = 50; % Dimension-less positive scalar integer parameter,
% i.e., G_MAX >= 1. It is the number of iterations
% (that is, the number of generations) performed
% by all the called genetic functions.
% Typically, G_MAX >= 20.
%
MN = V-2; % Number of elements of any V-long ternary
% task allocation vector: x that are mutated by
% the called genetic functions.
% It must be: 1 <= MN <= (V-2).
%
iteration_number = 5000; % It must be a POSITIVE INTEGER MULTIPLE of 5.
% It is the total number of the primal-dual iterations
% performed by the RAP function during each run of FogTracker.
% It must be ADDED to the list of the GLOBAL variables of
% the main program.
%
%
a_max_vec_FT = [1.15e-7,9.9e-8,9.3e-8];
% Dimension-less positive (1x3) real valued-vector.,
% It is the vector of the THREE tested speed-factors of the
% gradient-based iterations performed by the RAP function.
% Its size MUST BE 3. It must be ADDED to the list of the GLOBAL variables
% of the main program
%
jump1_WiFi = 0.0; % Non-negative scalar parameter that specifies the factor
jump1_CELL = 1.0; % of the up/down scaling applied to the maximum up/down
jump2_WiFi = 1.0; % bandwidths of the WiFi and Cellular connections. It must
jump2_CELL = 1.0; %or 0.0 % be added to the list of the GLOBAL variables of main program.
%
%
fprintf('..\n');
%
% End setup of the input parameters of MobiFogSim
%--------------------------------------------------------------------------