  Automatic resource scaling is one advantage of Cloud systems. Cloud systems
are able to scale the number of physical machines depending on user requests.
Therefore, accurate request prediction brings a great improvement in Cloud
systems' performance. If we can make accurate requests prediction, the
appropriate number of physical machines that can accommodate predicted amount
of requests can be activated and Cloud systems will save more energy by
preventing excessive activation of physical machines. Also, Cloud systems can
implement advanced load distribution with accurate requests prediction. We
propose an algorithm that predicts a probability distribution parameters of
requests for each time interval. Maximum Likelihood Estimation (MLE) and Local
Linear Regression (LLR) are used to implement this algorithm. An evaluation of
the proposed algorithm is performed with the Google cluster-trace data. The
prediction is implemented about the number of task arrivals, CPU requests, and
memory requests. Then the accuracy of prediction is measured with Mean Absolute
Percentage Error (MAPE).
