An Efficient Hybrid Job Scheduling Optimization (EHJSO) approach to enhance resource search using Cuckoo and Grey Wolf Job Optimization for cloud environment

Abstract

Cloud computing has now evolved as an unavoidable technology in the fields of finance, education, internet business, and nearly all organisations. The cloud resources are practically accessible to cloud users over the internet to accomplish the desired task of the cloud users. The effectiveness and efficacy of cloud computing services depend on the tasks that the cloud users submit and the time taken to complete the task as well. By optimising resource allocation and utilisation, task scheduling is crucial to enhancing the effectiveness and performance of a cloud system. In this context, cloud computing offers a wide range of advantages, such as cost savings, security, flexibility, mobility, quality control, disaster recovery, automatic software upgrades, and sustainability. According to a recent research survey, more and more tech-savvy companies and industry executives are recognize and utilize the advantages of the Cloud computing. Hence, as the number of users of the Cloud increases, so did the need to regulate the resource allocation as well. However, the scheduling of jobs in the cloud necessitates a smart and fast algorithm that can discover the resources that are accessible and schedule the jobs that are requested by different users. Consequently, for better resource allocation and job scheduling, a fast, efficient, tolerable job scheduling algorithm is required. Efficient Hybrid Job Scheduling Optimization (EHJSO) utilises Cuckoo Search Optimization and Grey Wolf Job Optimization (GWO). Due to some cuckoo species’ obligate brood parasitism (laying eggs in other species’ nests), the Cuckoo search optimization approach was developed. Grey wolf optimization (GWO) is a population-oriented AI system inspired by grey wolf social structure and hunting strategies. Make span, computation time, fitness, iteration-based performance, and success rate were utilised to compare previous studies. Experiments show that the recommended method is superior.

A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm

“…The model was trained using a 0.001 learning rate. We evaluate Mask-RCNN 27 , 28 , Faster-RCNN 29 , 30 , YOLO V3 31 , 32 , and YOLO V5 33 , 34 in comparison to our suggested system.…”

Advanced Methods for Maintaining and Managing the Life Cycle of Cloud Environments: Survey

“…Cloud computing allows organizations to optimize their IT expenditure by shifting from a capital-intensive model to a more agile operational expenditure approach. This flexibility not only reduces financial barriers to entry but also enables businesses to adapt swiftly to market changes and innovation [3].…”

Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System

“…HHO is a meta-heuristic optimizer algorithm utilized for the DenseHHO architecture [29,30], and was stimulated by the hunting behaviors of HHs. The HHO technique can resolve the problems of different optimization techniques, namely parameter optimization, function optimization, and feature selection for ML algorithms [31][32][33][34]. The subsequent steps need to be taken to implement the algorithm.…”