Workload forecasting framework for applications in cloud

With the development of cloud computing technics, an increasing number of applications prefer to be deployed in cloud. Load balancing becomes the key technicfor cloud provider to control the resources and cost. But using load balancing with real time data cannot react in time towards workload peak or valley. Thus, workload forecasting is presented to let the cloud provider to get ready for a possible workload change. There are already many kinds of predicting methods. In this article, we study the workload of applications in cloud and propose a workload forecasting framework. This framework monitors workloads of applications in real time, processes the data, and provides feedback of the predicted workload value according to historical data,guiding the cloudprovider to allocate resources.