A Novel Resource Productivity Based on Granular Neural Network in Cloud Computing

In recent years, due to the growing demand for computational resources, particularly in cloud computing systems, data centers’ energy consumption is continually increasing, which directly causes price rise and reductions of resources’ productivity. Although many energy-aware approaches attempt minimize energy, they cannot violation service-level agreements at same time. this paper, we propose a method using granular neural network, used model processing. This identifies physical hosts’ workloads before overflow can improve while also reducing agreements. Unlike other techniques that use single criterion, namely, worked on basis history processor, simultaneously all productivity rates criteria, is, processor rates, main memory, bandwidth. Extensive real-world simulations CloudSim simulator show high efficiency proposed algorithm.