Cloud computing is a new technology that has become a massive demand for computing solutions. Users can access computing
resources from anywhere with the help of the cloud. Nowadays,
more companies can hire cloud resources for storage and other
computational purposes so that infrastructure costs are considered to be exceedingly reduced. The resource allocation
mechanism based on the reservation method can be improved
with a useful forecasting model that can provide almost well for
developing resource allocation strategies. This can help to
improve customer needs and the growth of businesses that are
based on cloud computing. Service providers shall endure the
cost of resources reserved in the cloud. Resources are allocated to
reduce the associated costs. There are many algorithms
implemented to predict the allocated resources in the cloud and to
reduce the cost. This paper presents a Kalman filter with a
Neuro-fuzzy system composed of an ANFIS optimized by the
Quantum Genetic Algorithm. The algorithm was evaluated with
actual cluster tracking data in Google and demonstrated the
comparison method's weakness by showing much improved and
better predictions.