ENNEGCC-3D energy efficient scheduling algorithm using 3-D neural network predictor for Green Cloud Computing environment

Cloud Computing is an emerging technology which plays an important role in the digital world. Scheduling and load balancing are the two major issues in cloud computing. Scheduling is the process of allocating the cloud resources to the users whereas Load balancing is the efficient utilization of servers and ensure the availability of Cloud Servers to the users at any time. The proposed work ENNEGCC 3D gives a novel 3-Dimension Neural Network Predictor model to estimate the workload. The server statuses are dynamically changed according to the availability. ENNEGCC 3D work emphasizes Green Cloud Computing by reduced power Consumption and Heat. The packet drop is also into consideration and reduced the packet lost due to server availability. The experiment is carried out using 6 different processors using Cloud SIM Tool. ENNEGCC 3D avoids unwanted status change in server thus greatly reduces the power consumption.