Hybrid Genetic Algorithm for Cloud Computing Applications
This work addresses scheduling inefficiencies for cloud computing users, but it is incremental as it modifies standard methods without introducing a new paradigm.
The paper tackles job scheduling in cloud computing by developing a hybrid genetic algorithm with fuzzy theory to improve load balancing, reduce execution time, and lower costs. Experimental results show efficiency gains in execution time, cost, and average Degree of Imbalance compared to existing models.
In this paper with the aid of genetic algorithm and fuzzy theory, we present a hybrid job scheduling approach, which considers the load balancing of the system and reduces total execution time and execution cost. We try to modify the standard Genetic algorithm and to reduce the iteration of creating population with the aid of fuzzy theory. The main goal of this research is to assign the jobs to the resources with considering the VM MIPS and length of jobs. The new algorithm assigns the jobs to the resources with considering the job length and resources capacities. We evaluate the performance of our approach with some famous cloud scheduling models. The results of the experiments show the efficiency of the proposed approach in term of execution time, execution cost and average Degree of Imbalance (DI).