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

5 Multi-Flow Optimization
(a) Fixed w/p SEB (b) Poisson w/o SEB (c) Fixed w/ SEB
Figure 5.20: Latency Time of Single Messages T L (m i )
the computed waiting time (the higher the number of distinct items, the lower the waiting
time). For both message arrival rate distribution functions D = fixed (see Figure 5.20(a))
and D = poisson (see Figure 5.20(b)), the constraint is rarely exceeded. Essentially, the
latency of single messages varies from almost zero to the latency constraint, where the
few missed constraints (illustrates as plus in the plots) are caused by variations of the
execution time. Note that the latency constraint is explicitly a soft constraint, where
we guarantee that it is not exceeded with statistical significance. The reason is that we
compute the waiting time based on our cost estimation. If the real execution costs vary
slightly around this estimate, there exist cases where the constraint is slightly exceeded as
well. Furthermore, the constraint also holds for serialized external behavior (SEB), where
all messages show more similar latency times (see Figure 5.20(c)) due to serialization at the
outbound side. Thus, there is a lower variance of the latency time of single messages. Note
that this is a worst-case scenario. Typically, we use a waiting time ∆tw at W (P ′ , k ′ ) = ∆tw
that results in much lower latency time for single messages.
Figure 5.21: Latency in Overload Situations
The maximum latency constraint of single messages cannot be ensured if we are in
overload situations. Therefore, we investigated the influence of the message rate R on
the average latency time of single messages T L (m i ). We executed |M| = 1,000 messages
with a selectivity of sel = 1.0 for unoptimized and optimized plans varying the message
rate R ∈ [5 msg /s, 70 msg /s]. Figure 5.21 illustrates the results. For low message rates (no
overload situation), we observe a minimal average latency of about 220 ms for both plans.
For optimized plans, our optimizer achieved this by adjusting the waiting time to the
execution time of plan instances with batch size one. After the message rate exceeded
the execution time of the unoptimized plan, the average latency rapidly increases due
to growing input message queues and the related waiting time. In contrast, for MFO,
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