نوع مقاله : مقاله پژوهشی
نویسنده
عضو هیات علمی گروه مهندسی کامپیوتر، دانشکده فنی و مهندسی، دانشگاه آزاد اسلامی واحد شهرقدس، تهران، ایران.
چکیده
کلیدواژهها
عنوان مقاله [English]
نویسنده [English]
Abstract: Nowadays, the number of applications that require a short response time is increasing, and utilizing fog environment has recently received a lot of attention. Due to the dynamics of the resource usage pattern in most Internet of Things applications, a fixed location cannot be considered for the placement and execution of services in the fog environment, and therefore the services must be dynamically placed in the fog environment. The problem of placing required Internet of Things services in cloud devices with limited resources is known as an NP-hard problem. In this article, a dynamic method based on multi-objective genetic algorithm with non-dominated ranking is presented to solve this problem. In the proposed method, learning automata are used to improve genetic behavior and dynamically adjust mutation and crossover rates. The proposed method is simulated using iFogsim and the simulation results show that the proposed method has a better efficiency than the compared algorithms by simultaneously considering the three criteria of service delay, cost and energy consumption. In terms of cost, compared to the two CSA and LRFC methods, it has decreased by 11% and 21%, respectively. Also, in terms of the average service delay, compared to the CSA and HAFA methods, the proposed method has decreased by 7% and 15 %, respectively. In terms of energy consumption, the proposed method shows an improvement of at least 8% compared to other methods.
کلیدواژهها [English]
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