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Predicting cloud performance from user's perspective is a complex task,because of several factors involved in providing the service to the consumer.In this work, the response time of 10 real-world services is analyzed. We haveobserved long memory in terms of the measured response time of theCPU-intensive services and statistically verified this observation usingestimators of the Hurst exponent. Then, na\"ive, mean, autoregressiveintegrated moving average (ARIMA) and autoregressive fractionally integratedmoving average (ARFIMA) methods are used to forecast the future values ofquality of service (QoS) at runtime. Results of the cross-validation over the10 datasets show that the long-memory ARFIMA model provides the mean of 37.5 %and the maximum of 57.8 % reduction in the forecast error when compared to theshort-memory ARIMA model according to the standard error measure of meanabsolute percentage error. Our work implies that consideration of thelong-range dependence in QoS data can help to improve the selection of servicesaccording to their possible future QoS values.

Modeling and predicting measured response time of cloud-based web services using long-memory time series