DLMNN: A deep learning modified neural network for balancing the load of cloudlets on cloud

Abstract

Cloud Computing a revolution in the computing world, has enabled the users to utilize the services on the Cloud platform from anywhere at any time. As there is an increase in the demand for the utilization of a cloud environment, there are several challenges to be addressed by the companies or organizations to provide uninterrupted cloud services. To make the cloud services available without interruption, the challenge of balancing the load on cloud servers is a must. Proper allocation of load on the servers optimize the performance of the cloud and improves the efficiency to offer uninterrupted services. Recent studies have shown, cloud always needs to have a capable algorithm to distribute the load on servers of cloud architecture to be available to process cloudlets submitted by the customers. Our paper looks for a new load balancing algorithm that uses the concepts of neural network and is used to allocate the tasks in the cloud. The proposed algorithm consists of two steps. First, Features of tasks and cloud servers are extracted, and the necessary features are selected. The feature selection can be done by using MPCA. In the second step, the selected features are sent as input to the DLMNN algorithm to schedule the task in the cloud. Finally, the experimental results of the proposed DLMNN are compared with some existing algorithms.