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

4.6 Experimental Evaluation
Plan with Restricted k
In order to reveal the characteristics of vectorizing multiple plans, we further evaluated the
influence of restricting the number of execution buckets. This is applied if the cost-based
vectorization exceeds this computed maximum number of execution buckets. All other
aspects of vectorization for multiple plans is a combination of already presented effects.
(a) Number of Operators m
(b) Input Data Size d
Figure 4.25: Restricting k with Different Numbers of Operators and Data Sizes
The first sub-experiment analyzes the influence of the number of execution buckets on
the execution time of a message sequence with regard to varying number of plan operators
m. We fixed d = 1, t = 0, q = 50 and explicitly varied the number of execution buckets k.
Figure 4.25(a) shows the resulting execution time for a message sequence of n = 250. We
observe that, in a first part, an increasing number of execution buckets leads to decreasing
execution time. In a second part, a further increase of the number of execution buckets
led to an increasing execution time. As a result, there is an optimal number of execution
buckets, which increases depending on the number of operators. We annotated with k1, k2
and k3 the numbers of execution buckets that our A-CPV computed without restricting
k. Note that for m = 5, at most k = 5 execution buckets can be used.
In addition to this, we also analyzed the influence of the number of execution buckets
on the execution time with regard to different data sizes. Hence, we used the plan m = 20,
we fixed t = 0, q = 50 and we varied the data size d ∈ {1, 4, 7} (in 100 kB). Figure 4.25(b)
shows these results. We observe that the first additional execution bucket significantly
decrease the execution time, while after that, the execution time varies only slightly for an
increasing number of execution buckets. However, there is also an optimal point, where the
optimal number of execution buckets decreases with increasing data size due to increased
cache displacement. Again, we annotated the resulting number of execution buckets of
our A-CPV.
We might use randomized algorithms as heuristics for determining the number of buckets
k and for assigning operators to buckets. However, the presented experiments (Figure
4.25(a) and Figure 4.25(a)) show an interesting characteristic that prohibit such randomized
heuristics. While our cost-based vectorization approach finds the near-optimal
solution, a randomly chosen k might significantly decrease the performance, where the
influence of determining the best k increases with increasing number of operators.
In conclusion, the cost-based vectorization typically computes schemes, where k is close
to the optimal number of execution buckets with regard to minimal execution time of a
message sequence. Thus, we recommend using cost-based vectorization without restricting
k. However, for multiple deployed plans it is required to ensure the maximum constraint.
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