In this code a Cloud sim task scheduling master using Gradient swarm optimizer is proposed. Also, GSO (Gradient Free Optimizer), FCFS (First Come First Serve), Round Robin, SFJ (Shortest Job First) techniques are used in this code. The comparison of FCFS, Round Robin, SFJ provides the best result to the Gradient Free Optimizer. The purpose of this process is to calculate the best fitness value.
no data
Bestfitness value calculated.
#GradientSwarmOptimizer ,#GSO ,#FirstComeFirstServe ,# FCFS , #RoundRobin ,#ShortestJobFirst ,#SJF , #Best Fitness Value, #environment, #comparison, # process, # result, #algorithms, # minimized, #equally, efficient, #cloud, #Minimum, #energy, #power, #memory, #task, #performance, #approach, #response, #solutions, #Load, #model, #Comparative, #scheduler
[1]Pratap, R. and Zaidi, T., 2018, August. Comparative study of task scheduling algorithms through cloudsim. In 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 397-400). IEEE. [2]Yin, S., Ke, P. and Tao, L., 2018, May. An improved genetic algorithm for task scheduling in cloud computing. In 2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA) (pp. 526-530). IEEE.