Levy Flight Firefly Based Efficient Resource Allocation for Fog Environment  

作　　者：Anu Anita Singhrova 

机构地区：[1]Deenbandhu Chhotu Ram University of Science and Technology,Murthal,131039,Haryana,India

出　　处：《Intelligent Automation & Soft Computing》2023年第7期199-219,共21页智能自动化与软计算（英文）

摘　　要：Fog computing is an emergent and powerful computing paradigm to serve latency-sensitive applications by executing internet of things(IoT)appli-cations in the proximity of the network.Fog computing offers computational and storage services between cloud and terminal devices.However,an efficient resource allocation to execute the IoT applications in a fog environment is still challenging due to limited resource availability and low delay requirement of services.A large number of heterogeneous shareable resources makes fog computing a complex environment.In the sight of these issues,this paper has proposed an efficient levy flight firefly-based resource allocation technique.The levy flight algorithm is a metaheuristic algorithm.It offers high efficiency and success rate because of its longer step length and fast convergence rate.Thus,it treats global optimization problems more efficiently and naturally.A system framework for fog computing is presented,followed by the proposed resource allocation scheme in the fog computing environment.Experimental evaluation and comparison with the firefly algorithm(FA),particle swarm optimization(PSO),genetic algorithm(GA)and hybrid algorithm using GA and PSO(GAPSO)have been conducted to validate the effectiveness and efficiency of the proposed algorithm.Simulation results show that the proposed algorithm performs efficient resource allocation and improves the quality of service(QoS).The proposed algorithm reduces average waiting time,average execution time,average turnaround time,processing cost and energy consumption and increases resource utilization and task success rate compared to FA,GAPSO,PSO and GA.

关 键 词：Fog computing resource allocation FIREFLY IOT CLOUD 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]