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

2 Preliminaries and Existing Techniques
2.5 Summary and Discussion
To summarize, we classified existing work of specifying integration tasks, where we mainly
distinguish query-based, integration-flow-based and user-interface-oriented approaches.
Due to the emerging requirements of complex integration tasks that (1) stretch beyond
simple read-only applications, (2) involve many types of heterogeneous systems and applications,
and (3) require fairly complex procedural aspects, imperative integration flows are
increasingly used. Hence, we further classified the modeling, execution and optimization
of these integration flows in detail according to a generalized reference system architecture
of an integration platform for integration flows. Typically, an integration flow is modeled
as a hierarchy of sequences with control-flow semantics. The control-flow semantics subsumes
also implicit data-flow semantics by using instance-local, materialized intermediates
in the form of variables. With regard to the optimization of such integration flows, we
can summarize that mainly rule-based optimization approaches (optimize-once) have been
proposed so far. This optimization model has two major drawbacks. First, adaptation to
changing workload characteristics is impossible because the flow is only optimized once
during the initial deployment. Second, many cost-based optimization decisions cannot be
made statically in a rule-based fashion.
In contrast to the rule-based optimization of integration flows, there are numerous approaches
of adaptive query processing in different application areas. However, these approaches
are tailor-made for specific system types and their underlying assumptions of
execution characteristics. For example, plan-based adaptation in DBMS is based on the
assumption of long running queries over finite data sets, while continuous-query-based
adaptation in DSMS relies on the assumption of continuous queries over infinite tuple
streams. In contrast to these system types, integration flows exhibit the specific characteristics
of being deployed once and executed many times, where many independent
instances—with rather small amounts of data per instance—are executed over time. In
conclusion, the major research question is if we can exploit context knowledge of integration
flows in order to design a tailor-made optimization approach that takes into account
these specific characteristics of integration flows.
As a formal foundation, we defined the basic notation in the form of a meta model
for integration flows, including a message meta model that covers all static data aspects
and a flow meta model that precisely defines the plan execution characteristics as well as
interaction-, control-flow-, and data-flow-oriented operators. This meta model reflects the
common modeling and execution semantics of integration flows as well as their specific
transactional requirements and thus, all results of this thesis can be seamlessly applied to
other meta models as well. Furthermore, we specified example integration flows within the
context of the two major use cases of horizontal and vertical integration. These example
flows represent the main characteristics and different facets of integration flows and hence,
they are used as running examples throughout the whole thesis.
Putting it all together, there are existing approaches for query-based, integration-flowbased
and UI-oriented integration. From the perspective of optimization, there exist
tailor-made techniques for adaptive query processing. In contrast, the optimization of
integration flows is mainly rule-based. Thus, the focus and novelty of this thesis is the
cost-based optimization of integration flows that is strongly required in order to address
the high performance demands when executing integration flows.
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