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

2.1 Integration Flows
they group many independent service calls of an ActiveXML document that target the
same Web service into one service call and thus, in most cases, they reduce the amount
of transfered data. Note that all of these approaches for reducing the transfered data are
rule-based in the sense that rewriting is only applied once during the initial deployment
of an integration flow or the execution model was changed.
B. Control Flow Parallelization and Operator Reordering
In contrast to approaches that optimize the data transfer between the integration platform
and the external systems, further optimization approaches use control flow parallelization
in order to speed up integration flows by explicitly parallelizing processing steps or operator
reordering to reduce the size of intermediate results.
In general, all of those approaches analyze dependencies between tasks of a workflow and
then rewrite tasks with no dependencies between them to parallel subflows. For example,
Li and Zhan determine the critical path of a workflow with regard to the execution time and
then optimize only this part of the workflow [LZ05]. Typically, such rewritings are made
only once during the initial deployment (optimize-once model [BBD09]) and thus, they
are rule-based optimization approaches, where rewriting rules are often defined in terms of
algebraic equivalences [HML09, YB08]. For example, Behrend and Jörg proposed to use
the known Magic Sets technique for the rule-based optimization of incremental ETL flows
[Beh09, BJ10]. There, selection constants gathered in a subflow are propagated to other
subflows with common attributes in order to filter tuples as early as possible. In addition,
the XPEDIA system [BABO + 09] achieves partitioned parallelism by partitioning large
XML documents into multiple parts, evaluating the parts in parallel, and finally, merging
the results. This concept is a hybrid approach of control flow and data flow parallelism
because parallel subflows are used to evaluate independent data partitions.
Furthermore, Srivastava et al. proposed an algorithm for finding the best plan of Web
service calls (control-flow semantics) with regard to the highest degree of parallelism and
thus, lowest total execution time [BMS05, SMWM06]. In addition to the exploitation of
parallelism, they also include the selectivity of services as a metric for arranging these
services. However, costs of local processing steps are neglected and the optimal plan
is computed for each incoming ad-hoc Web service query (optimize-always optimization
model). Similar to this, Simitisis et al. proposed an optimization algorithm for ETL flows
[Sim04, SVS05] by modeling this optimization problem as a state-space search problem. In
addition to the parallelization of operators, they used techniques for merging, splitting and
reordering operators. Although they use a cost model, the approach is mainly rule-based
due to optimization on logical level and optimization is triggered whenever a flow instance
is requested. This optimize-always model [BBD09] is advantageous for long-running flow
instances. However, when executing many instances with rather small amounts of data—
as it is the case for typical workloads of integration flows—the optimize-always model falls
short due to the predominant optimization costs.
The BPEL/SQL approach uses, in addition to SQL-activity-specific rewriting rules,
parallelization techniques for the rewriting of SQL activities [Rei08]. This is an example
of a hybrid approach, where aspects of data transfer optimization and control flow
parallelization are combined in order to achieve highest performance.
To summarize, existing techniques of optimizing integration flows mainly try to decrease
the costs for accessing external systems and to increase the degree of parallelism. Thereby,
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