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

6.5 Experimental Evaluation
Workload Shifts wc Input Data Size d Optimization Interval ∆t
(a) Cumulative Execution Time (b) Cumulative Execution Time (c) Cumulative Execution Time
(d) Cumulative Opt. Time (e) Cumulative Opt. Time (f) Cumulative Opt. Time
(g) Number of Re-Optimizations (h) Number of Re-Optimizations (i) Number of Re-Optimizations
Figure 6.16: Scalability of Plan P 5 Varying Influencing Parameters
higher numbers of workload shifts the state alternate between optimal and non-optimal.
In contrast, on-demand re-optimization shows constant execution time, because it directly
reacts to any workload shift and the re-optimization time is no dominating factor, while
the execution time of periodical re-optimization degrades for increasing number of workload
shifts because we use non-optimal plans more often. Clearly, the optimization time
of on-demand re-optimization increases with increasing number of workload shifts (Figures
6.16(d) and 6.16(g)), while periodical re-optimization required an almost constant
number of re-optimizations steps. The small increase of optimization steps is reasoned by
the increased total execution time.
Second, we varied the input data size d ∈ [1, 7] (in 100 kB). Due to the increasing
absolute optimization benefit per single optimizations step with increasing data size,
the absolute improvement of on-demand re-optimization also increases compared to the
unoptimized execution and periodical re-optimization (Figure 6.16(b)). On-demand reoptimization
shows a constant re-optimization time (Figure 6.16(e)) because the number
of workload shifts was fixed to wc = 20. In contrast, the optimization time of period-
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