Cost-Based Optimization of Integration Flows - Datenbanken ...

3.5 Experimental Evaluation
We conducted further experiments where we executed n = 100,000 instances of the
plan P 8 and varied the scale factor data size d (in 100 kB) in order to simulate changing
workload characteristics. Figure 3.28(a) illustrates the monitored plan execution times
with annotated input data sizes d. Based on these statistics, we evaluated the influences
of workload aggregation methods, their parameters, and of the sliding time window size
in detail.
(a) Execution Time
(b) Workload Aggregation Method
(c) EMA Smoothing Parameter α
(d) Sliding Time Window Size ∆w
Figure 3.28: Influence of Parameters on the Sensibility of Workload Adaptation
First, Figure 3.28(b) shows the influence of the workload aggregation method, where
we fixed ∆w = 1,000 s and illustrate the estimated costs continuously (∆t = 1 s). The
real execution times contain several outliers and a major skew. Obviously, MA causes the
slowest adaptation, while WMA and EMA cause faster adaptation. This is reasoned by the
fact that MA (constant weights) computes a linear average, while WMA (linear weights) and
EMA (exponential weights) compute weighted averages over the sliding window, where the
latest items have higher influence. Further, LR causes the fastest adaptation due to extrapolation.
However, it is important to note that LR tends to strongly over- and underestimate
on abrupt workload shifts such as at the 80,000 th plan instance. The results support our
decision to use the exponential moving average (EMA) as default aggregation method due
to fast but robust workload adaptation. There, our default setting of the EMA parameter
α = 0.002 was set empirically based on the observed variance of plan execution times
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