A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces
Abstract
:1. Introduction
- Two hybrid algorithms based on Genetic Algorithm for multi-objective optimization for reducing makespan and energy optimization are proposed.
- The flowchart for the algorithms is presented and the algorithms are simulated using CloudSim simulator.
- Experiments are performed for FCFS, RR, GA, GA_FCFS and GA_RR algorithms for the real workload traces at Los Alamos National Lab (LANL).
2. Literature Review
2.1. Heuristic Approaches
2.2. Meta-Heuristics Approaches
3. Proposed Research Approach
3.1. Task Scheduling
3.2. Performance Parameters
3.2.1. Makespan
3.2.2. Resource Utilization (Ru)
4. Hybrid Genetic Algorithms
4.1. Task-Scheduling Policies
4.1.1. Time-Shared
4.1.2. Space-Shared
- Cloudlet Scheduler—Space-Shared;
- VM Scheduler—Time-Shared;
- PE to VM scheduling in host—Space-Shared.
4.2. Proposed Algorithms
4.2.1. Parameter Tuning in Genetic Algorithm
4.2.2. Encoding
Algorithm 1: Hybrid Virtual Machine Allocation Algorithm |
Input: |
-Cloudlet list: The tasks to be assigned to the VMs; |
-VM list: The total number of virtual machines; |
-Host list: Total number of hosts in the datacenter; |
-GA parameters: Selection, Crossover, Mutation. |
Output: |
-VM allocation list. |
Method: |
-Read the workload traces from Los Alamos National Lab (LANL); |
-Create a container for storing the virtual machines; |
-Create VMs with parameters (id, userId, mips, pesNumber, ram, bw, size, vmm) with space shared scheduling policy for cloudlets; |
-Create a container for storing the cloudlets; |
-Read Cloudlets from the workload traces. |
Main program: |
Step1: Initialize the CloudSim package before creating any entity; |
Step 2: Create Datacenters; |
Step 3: Create Datacenter Broker; |
Step 4: Create VMs and send them to broker along with the cloudlets: Sort the cloudlets based on their completion time; Sort the VMs based on their MIPS value; Assign longest task to fastest processor. |
Hybrid Genetic Algorithm: Step 1: Initial Population Generate the initial population of 20 chromosomes: Generate 19 chromosomes randomly; Take the 20th chromosome using FCFS. |
Step 2: Fitness Function Makespan and energy consumption are used for calculating the fitness function: |
Step 3: Selection Use roulette wheel selection. |
Step 4: Crossover Use single-point crossover. |
Step 5: Mutation Carry out the mutation operation. |
Step 6: Check the termination criteria If time <1,000,000 ms, Go back to step 2. Else Print the final allocation and the makespan. |
4.2.3. Fitness Function
Algorithm 2: Fitness Function |
Initialize: k = −1, w1 = 0 |
make[] = no_cloudlets |
makespans[] = no_chromosomes |
for i = 0 to no_chromosomes do |
makespans[i] = 0 ec[i] = 0 |
for i= 0 to no_VMs do |
make[i] = 0 |
for m = 0 to no_chromosomes do |
k++ |
for q= 0 to no_VMs do |
for n = 0 to no_cloudlets do |
if a[m][n] == q |
make[w1] = make[w1] + etc[n][q] |
w1++ |
w1 = 0 |
makespans[k] = maximum[make] ec[k] =maximum[ec] |
for w = 0 to no_chromosomes do |
makespans2[w] = (makespans[w] + maximum[ec])/2 |
4.2.4. Selection Operator
Algorithm 3: Selection |
total_fitness = 0 |
for w = 0 to no_chromosomes do total_fitness+=makespans[w] |
current_min_makespan = makespans2[0] |
for w = 0 to no_chromosomes do if (current_min_makespan > makespans2[w]) current_min_makespan = makespans2[w] |
throughput = no_cloudlets/current_min_makespan |
current_min_makespan = current_min_makespan + (1/throughput) |
Initialize count, count_same, ind, countm to 0 |
while (!(abs_value(min_makespan) == abs_value(current_min_makespan) && count_same == 3)) do for m = 0 to no_chromosomes do for n = 0 no_cloudlets do final_matrix[m][n] = b[m][n] for = 0 to no_chromosomes do final_makespans2[q] = makespans2[q]; min_makespan = total_fitness |
4.2.5. Crossover Operator
Algorithm 4: Crossover |
Initialize temp1 = 0 |
for m = 0 to no_chromosomes do |
for n = 0; o = no_VMs to n < no_VMs && o < no_cloudlets do temp1 = b[m][n] b[m][n] = b[m + 1][o] b[m + 1][o] = temp1 end for m = m+2 end for |
4.2.6. Mutation Operator
Algorithm 5: Mutation |
for m = 0 to no_chromosomes do |
b[m][rand.nextInt(no_VMs] = rand.nextInt(no_VMs) |
end for |
5. Results of the Experiment
5.1. Experimental Setup
5.2. Benchmark Techniques
6. Discussion
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Level 1 | Level 2 | Level 3 | Level 4 | Level 5 | |
---|---|---|---|---|---|
No. of Iterations | 500 | 700 | 1000 | 1200 | 1500 |
Crossover Probability | 0.2 | 0.4 | 0.6 | 0.8 | 0.9 |
Mutation Probability | 0.01 | 0.02 | 0.05 | 0.1 | 0.2 |
Type of Crossover | Single-point | Two-point | |||
Type of Mutation | Inversion | Bit Flip | |||
Type of Selection | Roulette Wheel | Tournament | Deterministic |
Source | Sum of Squares | D.F | Mean Square | F-Ratio | p-Level | F-Critical |
---|---|---|---|---|---|---|
Number of Iterations | 2.49 | 4 | 0.624 | 3.41 | 0.018 | 2.63 |
Crossover Probability | 64.32 | 4 | 16.08 | 9.112 | 0.00 | 2.63 |
Mutation Probability | 125.225 | 4 | 31.306 | 16.21 | 0.00 | 2.63 |
Type of Crossover | 0.886 | 1 | 0.886 | 0.64 | 0.44 | 5.12 |
Type of Mutation | 0.0028 | 1 | 0.0028 | 0.02 | 0.88 | 5.12 |
Type of Selection | 126.91 | 2 | 63.45 | 16.07 | 0.00 | 3.55 |
Levels of Variable “No. of Iterations” | Mean | Homogeneous Groups | ||||
1200 | 106.418 | A | ||||
1500 | 106.353 | A | B | |||
500 | 106.037 | A | B | C | ||
700 | 105.929 | B | C | D | ||
1000 | 105.868 | E | ||||
Levels of variable “Crossover Probability” | Mean | Homogeneous Groups | ||||
0.2 | 109.378 | A | ||||
0.4 | 108.517 | A | B | |||
0.9 | 108.23 | A | B | C | ||
0.6 | 107.364 | B | C | D | ||
0.8 | 106.037 | E | ||||
Levels of variable “Mutation Probability” | Mean | Homogeneous Groups | ||||
0.1 | 110.854 | A | ||||
0.02 | 108.517 | B | ||||
0.2 | 108.23 | B | C | |||
0.05 | 107.364 | B | C | D | ||
0.01 | 106.037 | E | ||||
Levels of variable “Type of Crossover” | Mean | Homogeneous Groups | ||||
Two Point | 106.458 | A | ||||
Single Point | 106.037 | B | ||||
Levels of variable “Type of Mutation” | Mean | Homogeneous Groups | ||||
Inversion | 106.061 | A | ||||
Bit Flip | 106.037 | B | ||||
Levels of variable “Type of Selection” | Mean | Homogeneous Groups | ||||
Deterministic | 110.7430 | A | ||||
Tournament | 109.9480 | A | B | |||
Roulette | 106.0370 | C | ||||
Significance level for all the test was taken as 0.05 |
Variable | Final Value |
---|---|
No. of Iterations | 1000 |
Crossover Probability | 0.8 |
Mutation Probability | 0.01 |
Type of Crossover | Single-point |
Type of Mutation | Bit Flip |
Type of Selection | Roulette Wheel |
VM Parameters | |
Parameter Name | Value |
Number of hosts | 02 per Datacenter |
Number of Virtual Machines | 14 |
Virtual Machine processing power in MIPS | 500 |
Virtual Machine RAM (MB) | 512 |
Virtual Machine Bandwidth (MBPS) | 10 |
Virtual Machine size (MB) | 10,000 |
Number of CPUs | 4 |
Host Parameters | |
Host RAM (GB) | 200 |
Host Storage | 10,000,000 |
Host Bandwidth (Mbps) | 100,000 |
Datacenter Properties | |
System Architecture | ×86 |
Virtual Machine Manager | Xen |
Time Zone | 10 |
Processing Cost | 3.0 |
Memory Usage Cost | 0.05 |
Operating System | Linux |
No. of Cloudlets | Makespan | Resource Utilization | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
FCFS | RR | GA | GA_FCFS | GA_RR | FCFS | RR | GA | GA_FCFS | GA_RR | |
100 | 19.99 | 20.99 | 10.65 | 5.69 | 3.27 | 0.2519 | 0.3276 | 0.7643 | 0.8645 | 0.9176 |
200 | 41.22 | 45.66 | 21.33 | 10.99 | 8.44 | 0.2976 | 0.3965 | 0.7128 | 0.8245 | 0.9256 |
300 | 88.29 | 89.42 | 40.56 | 25.54 | 16.28 | 0.2654 | 0.3168 | 0.5942 | 0.9102 | 0.9674 |
400 | 110.56 | 121.45 | 100.11 | 36.89 | 30.22 | 0.3012 | 0.3783 | 0.5734 | 0.8289 | 0.9125 |
500 | 159.29 | 170.21 | 132.87 | 59.76 | 48.55 | 0.3143 | 0.3989 | 0.5998 | 0.8912 | 0.9356 |
600 | 220.76 | 210.87 | 175.09 | 89.32 | 80.41 | 0.2986 | 0.2983 | 0.6983 | 0.8019 | 0.9876 |
700 | 269.86 | 260.75 | 201.86 | 130.44 | 119.53 | 0.339 | 0.3112 | 0.6102 | 0.911 | 0.9942 |
800 | 320.89 | 300.12 | 225.43 | 180.56 | 165.23 | 0.3218 | 0.3468 | 0.6324 | 0.9234 | 0.9876 |
900 | 375.87 | 370.22 | 295.21 | 240.85 | 225.69 | 0.3315 | 0.3451 | 0.6542 | 0.9625 | 0.9912 |
1000 | 425.97 | 420.65 | 312.98 | 280.33 | 272.45 | 0.2975 | 0.3256 | 0.6356 | 0.9123 | 0.9842 |
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Naz, I.; Naaz, S.; Agarwal, P.; Alankar, B.; Siddiqui, F.; Ali, J. A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces. Symmetry 2023, 15, 1025. https://doi.org/10.3390/sym15051025
Naz I, Naaz S, Agarwal P, Alankar B, Siddiqui F, Ali J. A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces. Symmetry. 2023; 15(5):1025. https://doi.org/10.3390/sym15051025
Chicago/Turabian StyleNaz, Insha, Sameena Naaz, Parul Agarwal, Bhavya Alankar, Farheen Siddiqui, and Javed Ali. 2023. "A Genetic Algorithm-Based Virtual Machine Allocation Policy for Load Balancing Using Actual Asymmetric Workload Traces" Symmetry 15, no. 5: 1025. https://doi.org/10.3390/sym15051025