Ajagbe, Sunday AdeolaOyediran, Mayowa O.Nayyar, AnandAwokola, Jinmisayo A.Al-Amri, Jehad2023-12-042023-12-042022-04-12http://repository.acu.edu.ng:8080/jspui/handle/123456789/356Cloud computing is a collection of disparate resources or services, a web of massive infrastructures, which is aimed at achieving maximum utilization with higher availability at a minimized cost. One of the most attractive applications for cloud computing is the concept of distributed information processing. Security, privacy, energy saving, reliability and load balancing are the major challenges facing cloud computing and most informa tion technology innovations. Load balancing is the process of redistributing workload among all nodes in a network; to improve resource utilization and job response time, while avoiding overloading some nodes when other nodes are underloaded or idle is a major challenge. Thus, this research aims to design a novel load balancing systems in a cloud computing environment. The research is based on the modification of the existing approaches, namely; particle swarm optimization (PSO), honeybee, and ant colony optimization (ACO) with mathematical expression to form a novel approach called P ACOHONEYBEE. The experiments were conducted on response time and throughput. The results of the response time of honeybee, PSO, SASOS, round-robin, PSO-ACO, and P-ACOHONEYBEE are: 2791, 2780, 2784, 2767, 2727, and 2599 (ms) respectively. The outcome of throughput of hon eybee, PSO, SASOS, round-robin, PSO-ACO, and P-ACOHONEYBEE are: 7451, 7425, 7398, 7357, 7387 and 7482 (bps) respectively. It is observed that P-ACOHONEYBEE approach produces the lowest response time, high throughput and overall improved performance for the 10 nodes. The research has helped in managing the imbalance drawback by maximizing throughput, and reducing response time with scalability and reliability.enACOcloud computingload balancingswarm intelligencePSOP-ACOHONEYBEhoneybee swarmArticle