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Resource allocation plays a pivotal role in Cloud Computing (CC), significantly impacting its overall performance.In order to fairly spread workload among servers, network ports, and hard drives, task scheduling is a critical module of CC.Cloud computing experiences request overloading as a result of dynamic computing over the internet.To address this challenge, a novel Cloud based Load balancing using Quantum artificial bee colony Optimization Algorithm has been proposed for task scheduling that can significantly enhance the effectiveness of cloud computing operations.The technique focuses on two important aspects of cloud computing: resource allocation and task scheduling.By achieving load balancing between servers, the proposed CLUQOA (Cloud based Load balancing Using Quantum artificial bee colony Optimization Algorithm) improves reliability and lowers expenses, latency, and response times.To show how effective it is, key performance parameters including makespan, resource usage, task migration, task execution time, and response time are assessed.The results of comparative comparisons with other approaches, such as HUNTER (Holistic resoUrce maNagemenT technique for Energyefficient cloud computing using aRtificial intelligence), FPNSO (Flower Pollination based Non-dominated Sorting Optimization), and ProHPA (Proactive Hybrid Pod Autoscaling), demonstrate the superiority of CLUQOA in terms of improving resource utilization (35.6%, 26.4%, and 13.9%, respectively) and reducing makespan (11.02%, 9.6%, and 10.4%, respectively).
A Fri, study studied this question.