LBO-HFA: A method of simulation to Load Balancing Optimization using the Hybrid Firefly Algorithm
محورهای موضوعی : Data mining
Sana Booshehrian
1
,
Ehsan Amiri
2
,
Javad Mohammadi Madavani
3
1 - Department of Computer Engineering, Jahrom University, Jahrom, Iran
2 - Department of Computer Engineering, Jahrom University, Jahrom, Iran
3 - Department of Computer Engineering, Islamic Azad University, Larestan Branch, Larestan, Iran
کلید واژه: Cloud Computing, Load Balancing, Firefly Algorithm, Optimization, Node, Genetic Algorithm,
چکیده مقاله :
The main goal of cloud computing is to achieve higher throughput on a large scale. Load balancing is always a challenge and requires a distributive solution. The response time criterion and energy consumption are evaluated by dynamically transferring the local workload from one machine to another or a less commonly used machine. The main purpose of the load balancing algorithm is to improve the response time by distributing the system's total load. Different algorithms are used in load balancing that can have different parameters. The most important features used are desirability and efficiency. In this report, we optimize the execution time in a set of tasks by examining the load balance parameters and using the Firefly algorithm. The proposed algorithm includes the improved firefly model, which is defined as two parts. The innovation of the present study includes improving the performance of the firefly algorithm and reducing the number of searches in this method, and it has been compared with other optimization algorithms from various aspects. The proposed algorithm enhances the firefly model by improving its performance and reducing the number of searches, as compared to other optimization algorithms. The research results show that the proposed method has a better balance in response time and memory than the GA, NSGA-II, and PSO methods. They also show that the load balance in processor efficiency has a growth of 6% compared to the GA, NSGA-II, and PSO.
The main goal of cloud computing is to achieve higher throughput on a large scale. Load balancing is always a challenge and requires a distributive solution. The response time criterion and energy consumption are evaluated by dynamically transferring the local workload from one machine to another or a less commonly used machine. The main purpose of the load balancing algorithm is to improve the response time by distributing the system's total load. Different algorithms are used in load balancing that can have different parameters. The most important features used are desirability and efficiency. In this report, we optimize the execution time in a set of tasks by examining the load balance parameters and using the Firefly algorithm. The proposed algorithm includes the improved firefly model, which is defined as two parts. The innovation of the present study includes improving the performance of the firefly algorithm and reducing the number of searches in this method, and it has been compared with other optimization algorithms from various aspects. The proposed algorithm enhances the firefly model by improving its performance and reducing the number of searches, as compared to other optimization algorithms. The research results show that the proposed method has a better balance in response time and memory than the GA, NSGA-II, and PSO methods. They also show that the load balance in processor efficiency has a growth of 6% compared to the GA, NSGA-II, and PSO.
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