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As cloud-based applications become increasingly solicited by companies and individuals, the competition between cloud providers that offer cloud services keeps increasing. To win this competition, cloud providers must provide sufficient computing resources that will satisfy users&#x2019; demand for every request. Workload prediction has been investigated extensively to resolve this issue using various techniques. This paper presents a workload prediction method called CANFIS, which combines the Savitzky-Golay (SG) filter and Chaotic time series analysis with the Adaptive Neural Fuzzy Inference System (ANFIS) to make predictions of cloud workloads. The SG filter is used to clean data from noise and outliers, and chaotic analysis is used to investigate the chaotic nature of workload and to build the improved ANFIS model. The proposed method is evaluated using real workload traces from web applications (i.e., Wikipedia and NASA Kennedy traces) and cluster applications (i.e., CPU and Memory of Google cluster). Experimental results show that the proposed CANFIS model can improve prediction accuracy compared to existing techniques, including simple ANFIS, Auto-Regressive Integrated Moving Average (ARIMA), Support Vector Machine (SVM), Long Short Term Memory (LSTM), and Neural Networks based methods. A statistical analysis is also performed using the Friedman test, along with Finner post hoc analysis to verify the efficiency of the proposed prediction model.

CANFIS: A Chaos Adaptive Neural Fuzzy Inference System for Workload Prediction in the Cloud