PSO-CALBA: Particle Swarm Optimization Based Content-Aware Load Balancing Algorithm in Cloud Computing Environment

Cloud computing provides hosted services (i.e., servers, storage, bandwidth, and software) over the internet. The key benefits of cloud are scalability, efficiency, cost reduction. challenge in is even distribution workload across numerous heterogeneous servers. Several scheduling load-balancing techniques have been proposed literature. These include heuristic-based, meta-heuristics-based, hybrid algorithms. However, most current load balancing schemes not content-aware they considering content-type user tasks). literature studies show that content type tasks can significantly improve balanced workload. In this paper, a novel approach named Particle Swarm Optimization based Content-Aware Load Balancing Algorithm (PSO-CALBA) proposed. PSO-CALBA scheme combines machine learning meta-heuristic algorithm performs classification utilizing file type. SVM classifier used to classify users' into different types like video, audio, image, text. (PSO) map user's on Cloud. has implemented evaluated using renowned Cloudsim simulation kit compared with ACOFTF DFTF. study shows significant improvement terms makespan, degree imbalance (DI).