SCEHO-IPSO: A Nature-Inspired Meta Heuristic Optimization for Task-Scheduling Policy in Cloud Computing

Abstract

Task scheduling is an emerging challenge in cloud platforms and is considered a critical application utilized by the cloud service providers and end users. The main challenge faced by the task scheduler is to identify the optimal resources for the input task. In this research, a Sine Cosine-based Elephant Herding Optimization (SCEHO) algorithm is incorporated with the Improved Particle Swarm Optimization (IPSO) algorithm for enhancing the task scheduling behavior by utilizing parameters like load balancing and resource allocation. The conventional EHO and PSO algorithms are improved utilizing a sine cosine-based clan-updating operator and human group optimizer that improve the algorithm’s exploration and exploitation abilities and avoid being trapped in the local optima problem. The efficacy of the SCEHO-IPSO algorithm is analyzed by using performance measures like cost, execution time, makespan, latency, and memory storage. The numerical investigation indicates that the SCEHO-IPSO algorithm has a minimum memory storage of 309 kb, a latency of 1510 ms, and an execution time of 612 ms on the Kafka platform, and the obtained results reveal that the SCEHO-IPSO algorithm outperformed other conventional optimization algorithms. The SCEHO-IPSO algorithm converges faster than the other algorithms in the large search spaces, and it is appropriate for large scheduling issues.