Accès libre

Research on Computational Load Balancing for Massively Parallel Tasks Based on Adaptive Iterative Algorithm

,  et   
29 sept. 2025
À propos de cet article

Citez
Télécharger la couverture

Figure 1.

Adaptive crossover probability
Adaptive crossover probability

Figure 2.

Adaptive mutation probability
Adaptive mutation probability

Figure 3.

Flow chart of improved genetic algorithm
Flow chart of improved genetic algorithm

Figure 4.

The solution of the three algorithms
The solution of the three algorithms

Figure 5.

The solution time and the cpoccupancy rate of the three algorithms
The solution time and the cpoccupancy rate of the three algorithms

Figure 6.

Test results for large-scale use cases
Test results for large-scale use cases

Figure 7.

Load statistics for tasks
Load statistics for tasks

Figure 8.

The result of the convergence of load inequality
The result of the convergence of load inequality

Figure 9.

The number of times required to spread to 99% of the process
The number of times required to spread to 99% of the process

Figure 10.

Different algorithms are overhead for different systems
Different algorithms are overhead for different systems

Figure 11.

Different algorithms are accelerating in different systems
Different algorithms are accelerating in different systems

Small-scale test case 1

Test number R1 R2 R3 R4 R5 R6 R7 Ri
Resource number
1 1 1 1 0 0 0 0 6
2 0 0 1 0 0 0 0 15
3 0 1 0 1 0 0 0 5
4 0 0 0 0 1 0 0 4
5 1 0 0 0 1 0 0 22
6 0 1 1 0 0 1 0 7
7 0 0 0 0 0 0 1 43
8 0 1 0 1 0 0 0 14