Task Scheduling Strategy of Heterogeneous Multicore Processor Based on Genetic Algorithm

Author(s):  
Juan Fang ◽  
Jiaxing Zhang ◽  
Shuaibing Lu ◽  
Hui Zhao ◽  
Di Zhang
Author(s):  
Ge Weiqing ◽  
Cui Yanru

Background: In order to make up for the shortcomings of the traditional algorithm, Min-Min and Max-Min algorithm are combined on the basis of the traditional genetic algorithm. Methods: In this paper, a new cloud computing task scheduling algorithm is proposed, which introduces Min-Min and Max-Min algorithm to generate initialization population, and selects task completion time and load balancing as double fitness functions, which improves the quality of initialization population, algorithm search ability and convergence speed. Results: The simulation results show that the algorithm is superior to the traditional genetic algorithm and is an effective cloud computing task scheduling algorithm. Conclusion: Finally, this paper proposes the possibility of the fusion of the two quadratively improved algorithms and completes the preliminary fusion of the algorithm, but the simulation results of the new algorithm are not ideal and need to be further studied.


2021 ◽  
Vol 30 ◽  
pp. 100513
Author(s):  
Dhritiman Mukherjee ◽  
Sudarshan Nandy ◽  
Senthilkumar Mohan ◽  
Yasser D. Al-Otaibi ◽  
Waleed S. Alnumay

Author(s):  
Ismail Zahraddeen Yakubu ◽  
Muhammad Aliyu ◽  
Zainab Aliyu Musa ◽  
Zakari Idris Matinja ◽  
I. M. Adamu

2021 ◽  
Vol 58 (5) ◽  
pp. 102676
Author(s):  
Samira Kanwal ◽  
Zeshan Iqbal ◽  
Fadi Al-Turjman ◽  
Aun Irtaza ◽  
Muhammad Attique Khan

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