Optimized Task Scheduling in Cloud Computing using Gazelle Optimization Algorithm

The topic of task scheduling in cloud environments is a hot one in current research. A cloud environment can enhance the core competitiveness and economic benefits of businesses and enterprises if massive tasks can be efficiently scheduled. NP-hard scheduling problems require finding optimal virtual machines with minimal makespan and optimal resource utilization. This problem is primarily concerned with designing an efficient intelligent search pattern to schedule tasks in the best available virtual machines. To improve performance in cloud computing, a task scheduler with Gazelle Optimization Algorithm (GOA) is proposed as a solution to the urgent need for an efficient scheduling method. The simulation results are compared to other swarm intelligent algorithms such as Grasshopper Optimization algorithm (GOA), Particle swarm optimization (PSO), Grey Wolf Optimizer (GWO) and Enhanced Multi-Verse Optimizer (EMVO). The evaluation results indicate that balanced task distribution reduces makepan significantly.