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

4.6 Experimental Evaluation
Figure 4.23: Vectorization Deployment Overhead
results, where we varied the number of operators m because all other scale factors do not
influence the deployment and maintenance costs. In general, there is a high performance
improvement using vectorization with a factor of up to seven. It is caused by the different
deployment approaches. The WFPE uses a compilation approach, where Java classes are
generated from the integration flow specification. In contrast to this, the VWFPE as well as
the CBVWFPE uses interpretation approaches, where plans are built dynamically with the
A-PV. The VWFPE always outperforms CBVWFPE because both use the A-PV but CBVWFPE
additionally uses the A-CPV in order to find the optimal number of execution buckets
k. Note that the additional costs for the A-CPV (that cause a break-even point with
the standard WFPE) occur periodically during runtime. Here, we excluded the costs for
flushing the pipelines because it depends mainly on the maximum constraint of the queues
and on the costs of the most time-consuming operator. In conclusion, the vectorization
of integration flows shows better runtime as well as often better deployment time performance
with regard to plan generation. Even in the case where a deployment overhead
exists, it is negligible compared to the runtime improvement we gain by vectorization.
In conclusion, the deployment and maintenance overhead is moderate compared to the
yielded performance improvement. Recall the evaluation results from Figure 4.21. It is
important to note that the presented performance of the cost-based vectorized execution
model already includes the costs for periodical re-optimization and statistics maintenance.
Parameters of Periodic Optimization
The resulting performance improvement of vectorization in the presence of changing workload
characteristics depends on the periodic re-optimization. This re-optimization can be
influenced by several parameters including the workload aggregation method, the sliding
time window size ∆w, the optimization period ∆t, and the maximum cost increase λ. In
this subsection, we evaluate the influence of λ with regard to the cost-based vectorization,
while the other parameters have already been evaluated in Chapter 3. Therefore, we conducted
an experiment, where we measured how increasing maximum costs influence the
number of execution buckets and thus, indirectly influence the elapsed time as well.
In a first sub-experiment, we fixed d = 1, t = 0, q = 50 and executed n = 250
messages with different λ and for different plans (with different numbers of operators m).
Figure 4.24(a) shows the influence of λ on the number of execution buckets k as well as on
the execution time. It is obvious that the number of execution buckets (annotated at the
top of each point) decreases with increasing λ because for each bucket, the sum of operator
costs must not exceed max + λ and hence, more operators can be executed by a single
bucket. Clearly, when increasing λ, the number of execution buckets cannot increase. In
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