Fractional Difference based Hybrid Model for Resource Prediction in Cloud Network
Shaifu Gupta, A. D. Dileep, Timothy A. Gonsalves · 2016
Cloud resource usage prediction is an important pre-requisite for optimal scheduling and load balancing. It is a very challenging task as a number of users with varied resource requests enter and leave the cloud network dynamically. Predicting resource usage in advance can aid service providers in better capacity planning to meet their service level objectives. In this paper, we propose a fractional differencing based method to capture long range dependence in time series data. The proposed model is evaluated on a Google cluster trace. Empirical results show that fractionally differencing the data gives better results as compared to non-fractionally differenced data. To take advantage of existing models, a hybrid method for resource usage prediction is proposed which combines the predictions of existing models to generate better forecasts.