Action-Based Load Balancing Technique in Cloud Network Using Actor-Critic-Swarm Optimization

Pseudocode 1

Pseudocode of our proposed algorithm.

HPSOAC Algorithm

Input: Server set as , VM set as and Task set as with γ, 𝜆, , .

Output: Reduce makespan Time, reduce energy consumption, increase usage of resource and balanced VM load.

Assumption:

Particle or Agent VM

Position Load of VM allocated on PM

Velocity Speed of task transfer

Pbest Individual VM performance

Gbest Optimal result

Iteration or Episode Time period

Information of Server and VM MIPS, Memory, CPU and Bandwidth

Information of Task Length and File size

Start

For i =1 to m

For j =1 to n

Incoming task is allocated on VM

// For Makespan

Calculate processing rate of VM, expected finishing time and task allocation time by applying Eq. (1), (2) and (3).

=

= /

= /

Find Finishing Time of each task on VM by the help of Eq. (4):

Calculate Makespan Time by utilizing Eq. (6):

//For Resource Utilization

Compute Resource Utilization by using Eq. (8):

End of for

End of for

//For Energy Consumption

Initialize active and sleep VM, utilization of both VM and server.

For j =1 to n

For s =1 to x

Compute active server by using Eq. (10):

Compute energy consumption of active server by using Eq. (11):

If ()

Put the server into sleep mode

Else

Wake-up the sleep server

End of if

Compute total energy consumption by using Eq. (17):

End of for

End of for

// Applying HPSOAC

//Initialize AC parameters

Initialize j, e, , , , and

// Initialize PSO parameters

Initialize the w, , , population size, the number of iterations,

For j =1 to n

Initialize individual best , current position and velocity of agent j ()

End of for

Initialize individual best , target position

For e =1 to

For j =1 to n

Observe the environment state .

For t =1 to

Agent takes action according to the Eq. (33):

Receive the current reward and perceives the next state .

Compute value function by using Eq. (40):

Compute TD error by using Eq. (28):

Update eligibility trace in Eq. (34):

Update critic parameters in Eq. (35):

Compute advantage function in Eq. (25):

Update policy gradient by using Eq. (41):

Update policy parameters in Eq. (42):

End of for

// Compute fitness value

Calculate cumulative reward as the fitness value of agent.

// Comparing current fitness with individual best

If

Then

End if

// Find out global best value from all individual best

Find

If

Then

Else ⃪ Optimal solution

End if

Update episode

Update weight and velocity according to Eq. (43) and (44)

End of for

End of for

End