Ensemble Load Balancing with Parallel Processing Algorithm in Cloud Computing
Kopparthi Poorna Sai Rama Krishna, K. Hari Krishna, Sreenu Naik Bhukya, Raghunadha Reddi Dornala, A. V. S. Asha · 2025
Cloud computing has become a more popular domain, providing online services in real-time applications. Optimal resource usage and effective task execution are critical for existing cloud models. This research mainly focused on developing the Ensemble load-balancing (ELB) algorithm combined with the parallel processing algorithm, which significantly manages load balancing among cloud users. The proposed load balancing algorithm primarily identifies the various issues with existing models, like low latency, improved throughput, and constant system maintenance. It combines priority load balancing with the MapReduce algorithm, also called a parallel processing algorithm. The proposed approach focused on improving the performance dynamically by randomly distributing tasks among the nodes based on factors such as task size, capacity of the server, and network latency. The MapReduce in this paper handles huge tasks by splitting the data into k parts and processing each task with priority load balancing. Simulation results show that the proposed approach shows effective load balancing with an efficient parallel processing algorithm in terms of speed, scalability, and overall system efficiency.