% Setting all the global variables for DAG #6

%

% DAG #6 describes a Map/Reducer workflow with V = 12 tasks.

%

% ***************************************************

% * Package: VirtFogSim *

% * Author: Enzo Baccarelli and Michele Scarpiniti *

% * Date: January, 2019 *

% * Version: 4.0 *

% ***************************************************

%

fprintf('..DAG #6..');

%--------------------------------------------------------------------------

% 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 = 12; % 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;

% k1 = 1;

% k1 = 0.5;

k2 = 1;

%

s = k1*c1*[1, 114, 300, 300, 300, 89, 50, 140, 140, 140, 85, 1];

% (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, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0;

0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1;

0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];

% (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, 15, 0, 0, 0, 0, 180, 0, 0, 0, 0, 0;

0, 0, 89.6, 89.6, 89.6, 0, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 180, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 180, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 180, 0, 0, 0, 0, 0, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 40;

0, 0, 0, 0, 0, 0, 0, 51.66, 51.66, 51.66, 0, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 97, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 97, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 97, 0;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 164;

0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0];

% (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; %1/0.3; % Scalar non-negative real parameter, i.e., TH_MIN_0 >= 0.

% It is the minimum required application throghput 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; % By Michele 220; 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; %60; % 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; %92; % 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; %by Michele = 3.2; %3.6;%3; % 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; %3.5; %3; % 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; %by Michele 3.0; %3.4; %3; % 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; %by Michele = 4.73e-20; %3.31e-19; %0.85e-18; % 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;%by Michele 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; %2.3; % Dimension-less positive scalar exponent

% of the dynamic power consumption of the WiFi NIC

% in the transmit mode.

%

zeta_RX_WiFi = 2.2; %2.1; % Dimension-less positive scalar exponent

% of the dynamic power consumption of the WiFi NIC

% in the receive mode.

%

zeta_TX_CELL = 2.45; %by Michele 2.42; %2.2; % 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; %by Michele 2.32; %2.1; % Dimension-less positive scalar exponent

% of the dynamic power consumption of the cellular

% 3G/4G NIC in the receive mode.

%

% Typically,

% zeta_TX_WiFi = 2.34, zeta_RX_WiFi = 2.18,

% zeta_TX_CELL (3G) = 2.14, zeta_RX_CELL (3G) = 2.1,

% zeta_TX_CELL (4G) = 2.16, zeta_RX_CELL (4G) = 2.15.

%

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; %5.0e-8; % 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;%by Michele 5*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;%by Michele 5*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))).

% chi_TX_WiFi = 2.6e-7;

% chi_RX_WiFi = 1.6e-7;

% chi_TX_CELL = 5.2e-7;

% chi_RX_CELL = 3.2e-7;

%

% Typically,

% chi_TX_WiFi = 8 x 10^(-7),

% chi_RX_WiFi = 5 x 10^(-7),

% chi_TX_CELL (3G) = 2.6 x 10^(-7),

% chi_RX_CELL (3G) = 1.6 x 10^(-7),

% chi_TX_CELL (4G) = 5.2 x 10^(-7),

% chi_RX_CELL (4G) = 3.2 x 10^(-7).

%

%

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 = 1.21e-7;

% a_max = 2.25e-6; %2.25e-5; %6.25e-7;

% a_max = 9.25e-7; %6.25e-8;

% a_max = 9.0e+8; %9.0e+9; % 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 = 120; % 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 = 20; % 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 = [1.295e-7,1.21e-7,1.1e-7];

% 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.

%------------------------------------------------------------------------