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

5.5 Experimental Evaluation
For comparison, the unoptimized plan was executed k ′ times and we measured the total
execution time W (P 2 ) · k ′ . This experiment has been repeated 100 times. Both unoptimized
and optimized execution show a linear scalability with increasing batch size k ′ with
the difference that for optimized execution, we observe a logical y-intercept that is higher
than zero. As a result, the unoptimized execution shows a constant relative execution
time, while the optimized execution shows a relative execution time that decreases with
increasing batch size and that tends towards a lower bound, which is given by the partial
plan costs of operators that do not benefit from partitioning. It is important to note
that (1) even for one-message-partitions the overhead is negligible and (2) that even small
numbers of messages within a batch significantly reduce the relative execution time.
Figure 5.19: Varying R and sel
Second, we evaluated the batch size k ′ according to different (fixed interval) message
rates R, and selectivities sel in order to validate our assumptions about the batch size
estimation of k ′ = R·∆tw under the influence of message queue partitioning. We processed
|M| = 100 messages with plan instances of P 2 , where all sub experiments were repeated
100 times with fixed waiting time of ∆tw = 10 s. Figure 5.19 shows the influence of the
message rate R on the average number of messages per batch. We observe (1) that the
higher the message rate, the higher the number of messages per batch, and (2) that the
selectivity determines the reachable upper bound. However, until this upper bound, the
batch size is independent of the selectivity because for higher selectivities we wait longer
(∆tw · 1/sel) until partition execution. Similarly, also an increasing waiting time showed
the expected behavior of linearly increasing batch sizes until the upper bound is reached.
Latency Time
Furthermore, we evaluated the latency influence of MFO. While the total latency time
of message sequences is directly related to the throughput and thus reduced anyway, the
latency time of single messages needs further investigation. In this subsection, we analyze
the given maximum latency guarantee and latency times in overload situations.
In detail, we executed |M| = 1,000 messages with plan P 2 using a maximum latency
constraint of lc = 10 s and measured the latency time T L (m i ) of single messages m i .
There, we fixed a selectivity of sel = 0.1, a message arrival rate of R = 5 msg /s, and
used different messages arrival rate distributions (fixed, poisson) as well as analyzed the
influence of serialized external behavior. In order to discuss the worst-case consideration,
we computed the waiting time ∆tw | T L (M ′ = k ′ /sel) = lc (that is typically only used
as a deescalation strategy). Here, ∆tw was computed as 981.26 ms because in the worst
case there are 1/sel = 10 different partitions plus the execution time of the last partition.
Note that the selectivity has no influence on the variance of message latency times but on
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