A Hybrid Method for Short-Term Host Utilization Prediction in Cloud Computing

Abstract

Dynamic resource scheduling is a critical activity to guarantee quality of service (QoS) in cloud computing. One challenging problem is how to predict future host utilization in real time. By predicting future host utilization, a cloud data center can place virtual machines to suitable hosts or migrate virtual machines in advance from overloaded or underloaded hosts to guarantee QoS or save energy. However, it is very difficult to accurately predict host utilization in a timely manner because host utilization varies very quickly and exhibits strong instability with many bursts. Although machine learning methods can accurately predict host utilization, it usually takes too much time to ensure rapid resource allocation and scheduling. In this paper, we propose a hybrid method, EEMD-RT-ARIMA, for short-term host utilization prediction based on ensemble empirical mode decomposition (EEMD), runs test (RT), and autoregressive integrated moving average (ARIMA). First, the EEMD method is used to decompose the nonstationary host utilization sequence into relatively stable intrinsic mode function (IMF) components and a residual component to improve prediction accuracy. Then, efficient IMF components are selected and then reconstructed into three new components to reduce the prediction time and error accumulation due to too many IMF components. Finally, the overall prediction results are obtained by superposing the prediction results of three new components, each of which is predicted by the ARIMA method. An experiment is conducted on real host utilization traces from a cloud platform. We compare our method with the ARIMA model and the EEMD-ARIMA method in terms of error, effectiveness, and time-cost analysis. The results show that our method is a cost-effective method and is more suitable for short-term host utilization prediction in cloud computing.

A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

“…Data preprocessing should be performed to smooth the extremely nonstationary sequences to enhance the prediction accuracy. Our previously proposed EEMD-ARIMA and EEMD-RT-ARIMA algorithms improve the prediction accuracy through decomposing a nonstationary sequence into a few relatively stationary component sequences via EEMD method [5,6]. The main difference between EEMD-RT-ARIMA and EEMD-ARIMA methods is that EEMD-RT-ARIMA method reduces the cumulative error and the prediction time by selecting and reconstructing the component sequences with similar characteristics into few component sequences based on RT values when the original sequence has weak fluctuation.…”

“…(1) We propose a runs test (RT)-based adaptive prediction algorithm for resource requests. This algorithm is built based on our previously studied ensemble empirical mode decomposition (EEMD)-Autoregressive Integrated Moving Average model (ARIMA) and EEMD-RT-ARIMA algorithms [5,6], and it can select a more accurate algorithm to implement the short-term prediction of resource requests via an adaptive prediction strategy. (2) We propose a proactive resource allocation strategy that combines the active prediction and the passive response of resource requests, which can allocate resources in advance for the future sudden resource requests to guarantee the timelessness of the resource allocation.…”

Online Workload Burst Detection for Efficient Predictive Autoscaling of Applications

“…Any regression-based estimation model can use the predicted window size method for the prediction of resource utilization with minimum error. Similarly, Chen and Wang [27] proposed a method to improve the accuracy and prediction time of resource utilization. The authors use three different components including Ensemble Empirical Mode Decomposition, Run Tests, and ARIMA to improve the prediction results.…”

Predicting Multi-Attribute Host Resource Utilization Using Support Vector Regression Technique

“…In the cloud computing area, we need a speedy and simple techniques for cloud workload and resources usage prediction, which have significant impact on the VM placement process. Chen and Wang [27] presented a hybrid forecasting approach. The proposed method combined the EEMD theory with the ARIMA technique for predicting future utilization of the host resource (CPU).…”