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

K-MaxSDev) gains a large improvement on the average accuracy of interval
forecasting over the other representative forecasting strategies. The average
accuracy of our K-MaxSDev is 80% while that of the other strategies are mainly
around 40% to 50%. Among the other 6 strategies, AR behaves the best while the
others have similar performance except MEAN which behaves poorly compared
with the others. However, such results actually demonstrate the highly dynamic
performance of the underlying services in cloud workflow systems.
To conclude, in comparison to other representative forecasting strategies in
traditional workflow systems, our forecasting strategy can effectively address the
problems of limited sample size and frequent turning points, and hence achieve
higher accuracy.
4.5 Summary
Interval forecasting for activity durations in cloud workflow systems is of great
importance since it is related to most of cloud workflow QoS and non-QoS
functionalities such as load balancing, workflow scheduling and temporal
verification. However, predicting accurate duration intervals is very challenging due
to the dynamic nature of cloud computing infrastructures. Most of the current work
focuses on the prediction of CPU load to facilitate the forecasting of computation
intensive activities. In fact, the durations of scientific cloud workflow activities are
normally dominated by the average performance of the workflow system over their
lifecycles. Therefore, the forecasting strategies in cloud workflow systems should be
able to adapt to the characteristics of scientific cloud workflow activities.
In this chapter, rather than fitting conventional linear time-series models which
are not ideal, we have investigated the idea of pattern based time-series forecasting.
Specifically, based on a novel non-linear time-series segmentation algorithm named
K-MaxSDev, a statistical time-series pattern based forecasting strategy which
consists of four major functional components: duration series building, duration
pattern recognition, duration pattern matching, and duration interval forecasting,
have been proposed. K-MaxSDev includes an offline time-series pattern discovery
process and an online forecasting process. The simulation experiments conducted in
our SwinDeW-C cloud workflow system have demonstrated that our time-series
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