PRIORITIZED GA-PSO ALGORITHM FOR EFFICIENT RESOURCE ALLOCATION IN FOG COMPUTING

Everyone is connected to internet these days and internet plays a vital role in daily lives of humans.Ubiquitous devices like smart phones makes internet accessible anywhere.Key issue into day's computing environment is how to efficiently address the demands of billions of internet users.In computing environment, extension of cloud computing has been introduced, named as Fog computing.Fog computing provides services in the proximity of end user with very low latency.It is an intermediate layer between cloud and IoT devices.Fog layer's main objective is to reduce the delay and improve the response time for user's request.To use the Fog Computing effectively, the resources should be allocated in an efficient way, while catering to the user requests.In this paper, a hybrid Prioritized Genetic Particle Swarm Optimization (P-GA-PSO) algorithm has been proposed for efficient resource allocation in fog computing.This proposed algorithm allocates tasks to the resources efficiently, consequently reducing delay, waiting time and energy consumption by 8.73%, 22.65% and 17.81% respectively as well as improving resource utilization by 0.54% in comparison to GA.Similarly, the proposed algorithm when compared with Round Robin algorithm showed reduced delay, waiting time and energy consumption by 3.90%, 21.99% and 1.68% respectively as well as improving resource utilization by 12.51%.Further, the quantitative analysis showed that the proposed algorithm performs better than GA and round-robin algorithms and moves towards optimal solutions faster than these algorithms.