Hybrid Cuckoo Search Algorithm for Scheduling in Cloud Computing

Abstract

Cloud computing has gained widespread popularity over the last decade. Scheduling problem in cloud computing is prejudiced due to enormous demands of cloud users. Meta-heuristic techniques in cloud computing have exhibited high performance in comparison to traditional scheduling algorithms. This paper presents a novel hybrid Nesterov Accelerated Gradient-based Cuckoo Search Algorithm (NAGCSA) to address the scheduling issue in cloud computing. Nesterov Accelerated Gradient can address trapping at local minima in CSA by updating the position using future approximation. The local search in the proposed algorithm is performed by using Nesterov Accelerated Gradient, while the global search is performed by using levy flights. The amalgamation of NAG and CSA helps in cost reduction and time-saving for users. The simulation has been carried out on the CloudSim tool on three different real datasets; NASA, HPC2N, and SDSC. The results of the proposed hybrid algorithm have been compared with state-of-art scheduling algorithms (GA, PSO, and CSA), and statistical significance is carried on mean, standard deviation, and best for each algorithm. It has been established that the proposed algorithm minimizes the execution cost and makespan, hence enhancing the quality of service for users.