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

4 Vectorizing Integration Flows
Based on the general cost-based optimization framework, in this chapter, we present
the vectorization of integration flows [BHP + 09a, BHP + 09b, BHP + 11] as a control-floworiented
optimization technique that is tailor-made for integration flows. This technique
tackles the problem of low CPU utilization imposed by the instance-based plan execution
of integration flows. The core idea is to transparently rewrite instance-based plans into
vectorized plans with pipelined execution characteristics in order to exploit pipeline parallelism
over multiple plan instances. Thus, this concept increases the message throughput,
while it still ensures the required transactional properties. We call this concept vectorization
because a vector of messages is processed at-a-time.
In order to enable vectorization, we first describe necessary flow meta model extensions
as well as the rule-based plan vectorization that ensures semantic correctness; i.e., the
rewriting algorithm preserves the serialized external behavior. Furthermore, we present
the cost-based vectorization that computes the optimal grouping of operators to multithreaded
execution buckets in order to achieve the optimal degree of pipeline parallelism
and hence, maximize message throughput. We present exhaustive, heuristic, and constrained
computation approaches. In addition, we also discuss the cost-based vectorization
for multiple deployed plans and we sketch how this rather complex optimization technique
is embedded within our periodical re-optimization framework. Finally, the experimental
evaluation shows that significant throughput improvements are achieved by vectorization,
with a moderate increase of latency time for individual messages. The cost-based vectorization
further increases this improvement and ensures robustness of vectorization.
4.1 Motivation and Problem Description
In scenarios with high load of plan instances, the major optimization objective is often
throughput maximization, where moderate latency times are acceptable [UGA + 09]. Unfortunately,
despite the optimization techniques on parallelizing subflows, instance-based
plans of integration flows, typically, do not achieve a high CPU utilization.
Problem 4.1 (Low CPU Utilization). The low CPU utilization is mainly caused by (1)
significant waiting times for external systems (for example, the plan instance is blocked,
while executing external queries), (2) the trend towards multi- and many-core architectures,
which stands in contrast to the single-threaded execution of instance-based integration
flows, and (3) the IO bottleneck due to the need for message persistence to enable
recoverability of plan instances.
In conclusion of Problem 4.1 in combination with the existence of many independent plan
instances, there are optimization opportunities with regard to the message throughput,
which we could exploit by increasing the degree of parallelism. Essentially, we could
leverage four different types of parallelism to overcome that problem, where we additionally
use the classification [Gra90] of horizontal (parallel processing of data partitions) and
vertical parallelism (pipelining):
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