Xiao Liu PhD Thesis.pdf - Faculty of Information and Communication ...

4.2 Related Work and Problem Analysis
4.2.1 Related Work
In this thesis, we focus on time-series based interval forecasting strategies. Five
service performance estimation approaches including simulation, analytical
modelling, historical data, on-line learning and hybrid, are presented in [98]. In
general, time-series forecasting belongs to historical data based approaches.
Currently, much work utilises multivariate models and focuses on the prediction of
CPU load since it is the dominant factor for the durations of computation intensive
activities. Typical linear time-series models such as AR, MA and Box-Jenkins are
fitted to perform host load prediction in [31] where it claims that most time-series
models are sufficient for host load prediction and recommends AR(16) which
consumes miniscule computation cost. The work in [52] proposes a one-step-ahead
and low-overhead time-series forecasting strategy which tracks recent trends by
giving more weight to recent data. A hybrid model which integrates the AR model
with confidence interval is proposed in [97] to perform n-step-ahead load prediction.
A different strategy proposed in [85] predicts application duration with historical
information where search techniques are employed to determine those application
characteristics that yield the best definition of similarity. The work in [3]
investigates extended forecast of both CPU and network load on computation grid to
achieve effective load balancing. The authors in [97] mention the problem of turning
points which causes large prediction errors to time-series models. However, few
solutions have been proposed to investigate more affecting factors and handle
turning points. Different from the above work, there are a few literatures utilise
univariate models. NWS [93] implements three types of univariate time-series
forecasting methods including mean-based, median-based and autoregressive
methods, and it dynamically chooses the one exhibiting the lowest cumulative error
measure at any given moment to generate a forecast. Hence, NWS automatically
identifies the best forecasting methods for any given resource. The work in [33]
proposes a Dynamic Exponential Smoothing (DES) method which forecasts the
future duration only based on its past values. The basic motivation for DES is to
handle the problems of sudden peaks and level switches. DES computes the optimal
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