Empirical Prediction Models for Adaptive Resource Provisioning in the Cloud

Cloud computing allows dynamic resource scaling for enterprise online transaction systems, one of the key characteristics that differentiates cloud from the traditional computing paradigm. However, initializing a new virtual instance in cloud is not instantaneous; the cloud hosting platforms introduce several minutes delay in the hardware resource allocation. In this paper, we develop prediction-based resource measurement and provisioning strategies using Neural Network and Linear Regression to satisfy upcoming resource demands. Experimental results demonstrate that the proposed technique offers more adaptive resource management for applications hosted in cloud environment, an important mechanism to achieve on-demand resource allocation in the cloud. Keywords