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

4.3 Cost-Based Vectorization
Join (o4)
[in: msg1,msg3, out: msg4]
Assign (o5)
[in: msg4, out: msg5]
Invoke (o6)
[service s3, in: msg5]
Copy (oc)
[in: msg1, out: msg1]
Execution Bucket 4
Execution Bucket 5
Execution Bucket 6
Execution Bucket 1
Assign (o2)
[in: msg1, out: msg2]
Execution Bucket 2
Invoke (o3)
[service: s4, in: msg2, out: msg3]
(a) Vectorized Plan P ′ 2
Execution Bucket 3
Join (o4)
[in: msg1,msg3, out: msg4]
Assign (o5)
[in: msg4, out: msg5]
Invoke (o6)
[service s3, in: msg5]
Copy (oc)
[in: msg1, out: msg1]
Execution Bucket 3
Execution Bucket 4
Execution Bucket 1
Assign (o2)
[in: msg1, out: msg2]
Invoke (o3)
[service: s4, in: msg2, out: msg3]
Execution Bucket 2
(b) Cost-Based Vectorized Plan P ′′
2
Figure 4.9: Example Cost-Based Plan Vectorization
(t 1 (b i , o i ) ≤ t 0 (p ′′
i+1 , b i)) must hold. We define that (l bi ≥ 1) ∧ (l bi ≤ m) and ∑ |b|
i=1 l b i
= m
and that each operator o i is assigned to exactly one bucket b i .
An instance-based plan P is a specific case of the cost-based vectorized plan P ′′ , with
k = 1 execution buckets. Similarly, the fully vectorized plan P ′ is also a specific case of the
cost-based vectorized plan P ′′ , with k = m execution buckets, where m denotes the number
of operators. Figure 4.10 illustrates the resulting spectrum of cost-based vectorization.
instance-based
plan
k=1 2 3
cost-based vectorized plan
m-2 m-1
Figure 4.10: Spectrum of Cost-Based Vectorization
vectorized
plan
k=m
At this point, we need to define the optimization objective φ, where in general, arbitrary
objectives could be used. However, the goal of the vectorization optimization technique
is message throughput improvement. Thus, the optimization objective is to reach the
highest degree of pipeline parallelism with a minimal number of threads.
The core idea of this objective is illustrated in Figure 4.11. In case of an instance-based
plan, all operators, except for parallel subflows, are included in the critical path that is
shown as gray-shaded operators. In contrast, in case of a vectorized plan that includes
a sequence of operators o with data dependencies between these operators, the execution
time mainly depends on the most time-consuming operator o max with W (o max ) =
max m i=1 W (o i). The reason is that queues in front of this most time-consuming operator
reach their maximum constraints—in case of full system utilization—and thus, the costs
of a vectorized plan are computed with W (P ′ ) = (n + m − 1) · W (o max ). This execution
characteristic, that the work-cycle of a pipeline depends on its most time-consuming subtask,
is known from other research areas (e.g., databases and operating systems) as the
convoy effect [Ros10, BGMP79]. As a result of this effect, the work cycle of the vectorized
plan is given by W (o max ) with the time period between the start of two subsequent plan
instances W (o max ) = t 0 (p i+1 )−t 0 (p i ). For example, operator o 3 dominates the work cycle
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