An Enhanced Load Balancing Model in Cloud Computing Environment

Abstract

Cloud computing has shifted processing and data from desktop and portable PCs to massive data centers, attracting significant interest not only in academia but also in business. Millions of people use the internet today, and this rapid expansion has resulted in the storing of enormous amounts of data online, resulting in an increase in web traffic. As a result, network congestion, online service delays, and delayed response times have arisen. The necessity for high scalability, availability, and rapid response has increased as the demand for high-speed processing has increased. As a result, load balancing is required in the cloud computing environment. In this paper, two load balancing algorithms (Round robin and Power Aware algorithms) are integrated to create a hybrid model (RR PALB). In contrast to existing models, the RR PALB model has all nine (9) parameters used in measuring the success of load balancing algorithms as positive, which is an advantage and thus improved overall performance. The hybrid model was developed using the CloudSim toolkit in the JAVA programming language in a cloud computing environment. The Cloud Simulation environment was used to run the performance evaluation trials. The hybrid model's accuracy was 99.42 percent, compared to 98.64 percent and 70.77 percent for the Round Robin and Power Aware models, respectively.