Employing the Powered Hybridized Darts Game with BWO Optimization for Effective Job Scheduling and Distributing Load in the Cloud-Based Environment

Abstract

One of the most frequent issues in cloud computing systems is job scheduling, which is designed to efficiently reduce installation time and cost while concurrently enhancing resource utilisation. Limitations such as accessible implementation costs, high resource utilisation, insufficient make-span, and fast scheduling response lead to the Nondeterministic Polynomial (NP)-hard optimisation problem. As the number of combinations along with processing power increases, job allocation becomes NP-hard. This study employs a hybrid heuristic optimisation technique that incorporates load balancing to achieve optimal job scheduling and boost service provider performance within the cloud architecture. As a result, there are many less problems with the scheduling process. The suggested work scheduling approach successfully resolves the load balancing issue. The suggested Hybridised Darts Game-Based Beluga Whale Optimisation Algorithm (HDG-BWOA) assists in assigning jobs to the machines according to workload. When assigning jobs to virtual machines, factors such as reduced energy usage, minimised mean reaction time, enhanced job assurance ratio, and higher Cloud Data Centre (CDC) resource consumption are taken into account. By ensuring flexibility among virtual computers, this job scheduling strategy keeps them from overloading or underloading. Additionally, by employing this method, more activities are effectively finished before the deadline. The effectiveness of the proposed configuration is guaranteed using traditional heuristic-based job scheduling techniques in compliance with multiple assessment metrics.