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

2.3 Integration Flow Meta Model
teristics are non-blocking operators and the need for state-awareness (e.g., state migration
on plan rewriting). Due to processing infinite streams, re-optimization is by definition
intra-operator or per-tuple routing, where re-optimization can be done asynchronously.
2.2.4 Integration Flow Optimization
As we have shown in Subsection 2.1.5, existing techniques for the optimization of integration
flows are mainly rule-based (optimize-once) [LZ05, VSS + 07, BJ10] and thus, do
not address re-optimization, or they follow an optimize-always model [SVS05, SMWM06].
However, similar to the categories of plan-based adaptation in DBMS and continuousquery-based
adaptation in DSMS, integration flows exhibit certain specific characteristics
that could be exploited for a more efficient re-optimization approach.
First, integration flows are deployed once and executed many times. Due to the execution
of many instances with rather small amounts of data (that stands in contrast to
plan-based adaptation in DBMS), there is no need for inter-operator or intra-operator reoptimization.
Second, in contrast to CQ-based adaptation in DSMS, many independent
instances of an integration flow are executed over time. Due to this execution model of independent
instances, there is no need for state migration because a plan can be exchanged
between two subsequent instances with low costs.
Based on the specific characteristics, integration flows require incremental statistic
maintenance and inter-instance (inter-query) re-optimization. The advantages would be
(1) the asynchronous optimization independent of executing certain instances, (2) the
fact that all subsequent instances rather than only the current query benefit from reoptimization,
and (3) the inter-instance plan change without the need of state migration.
To summarize, we infer that the specific characteristics of DBMS, DSMS and integration
platforms require tailor-made optimization approaches. While there exist sophisticated approaches
for plan-based adaptation in DBMS and continuous-query-based adaptation in
DSMS, to the best of our knowledge, there does not exist any optimization approach of
integration flows that allows the continuous adaptation to changing workload characteristics.
This lack of a tailor-made cost-based optimization approach for integration flows is
addressed in this thesis.
2.3 Integration Flow Meta Model
Based on the literature review of integration flows, we now define the integration flow
meta model that is used as our formal foundation throughout the whole thesis. On the
one side, we introduce the basic notation of integration flows including the message meta
model and the flow meta model as well as the execution semantics of integration flows. On
the other side, we discuss specific transactional properties of integration flows that must
be ensured when rewriting such flows.
2.3.1 Notation of Integration Flows
As the basic notation of integration flows, essentially, we use an extension of the so-called
Message Transformation Model (MTM) [BWH + 07, BHW + 07]. This integration flow meta
model consists of two vital parts. First, the message meta model describes the structural
aspects, that is, the structure of data objects (materialized intermediates) processed by an
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