Cloud computing is known as a internet based model for providing shared and on demand accessing of the resources (CPU, memory, processor, etc.). It is known as a dynamic service provider using very large scalable and virtualized resources over the Internet. With the help of cloud computing and virtualization technology, large number of online services can run over virtual machines (VMs), which in turn will reduce the number of physical servers. However, maintaining and managing the resources demand dynamically for these virtual machines with changing demand of resources while maintaining the service level agreement (SLA) is a challenging task for the cloud provider. Dynamic resource scheduling is a way to help manage the resource demand for virtual machines to handle variable workload without SLA violation. In this paper, we introduce Neural based prediction strategy to enable elastic scaling of resources for cloud systems. Unlike traditional static approach which do not consider the VM workload variability in account and dynamic approaches which sometimes predict under estimate of resources or over estimate of the resource, here we consider both workload fluctuations of VMs and prediction estimation problem into account. Neural based prediction strategy will first predict the VM resource demand based on Artificial Neural Network (ANN) model, to achieve resource allocation for cloud applications on each VM. Once the prediction is done, we than apply dynamic resource scheduling to consolidate the virtual machines with adaptive resource allocation, to reduce the number of active physical server while satisfying the SLA
