Resource Utilization Prediction in Cloud Computing using Hybrid Model
K C Anupama, B. R. Shivakumar, R. Nagaraja
Abstract
In cloud environment, maximum utilization of resource is possible with good resource management strategies. Workload prediction plays a vital role in estimating the actual resource required for successful execution of an application on cloud. Most of the existing works concentrated on predicting workloads which either showed clear seasonality/trend or for irregular workload patterns. This paper presents a new perspective in forecasting both seasonal and non-seasonal workloads. To accomplish this, a hybrid prediction model which is a combination of statistical and machine learning technique is proposed. Suppose the seasonality exists in the workload pattern, Seasonal Auto Regressive Integrated Moving Average (SARIMA) model is applied for prediction. For non-seasonal workloads Long Short-Term Memory networks (LSTM) or AutoRegressive Integrated Moving Average (ARIMA) model is used based on the results of normality test. This paper presents a prediction model which forecasts the actual resource required for diverse time intervals of daily, hourly and minutes utilization. The experimental results confirm that accuracy of the prediction of LSTM model outperformed ARIMA for irregular workload patterns. The SARIMA model accurately forecasts the resource usage for forthcoming days. This work actually helps the cloud service provider (CSP) to analyze the workload and predict accordingly to avoid over or under provisioning of the cloud resources.