Virtual resource prediction in cloud environment: A Bayesian approach

摘要

With increase in requirement for dynamic execution of user's application in cloud, resource prediction techniques are gaining a lot of importance as the foundation for online capacity planning and virtualized resource management in data centers. There is a wide scope for the development of accurate resource requirement prediction methods to ensure that the virtualized resources do not suffer from over or under-utilization. We propose a Bayesian model to determine short and long-term virtual resource requirement of the CPU/memory intensive applications on the basis of workload patterns at several data centers in the cloud during several time intervals. However, the model is applied to predict resource(s) of all applications in general.