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

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3 Fundamentals of Optimizing Integration Flows @type='MATMAS04' Translation (o3) [in: msg1, out: msg2] Receive (o1) [service: s3, out: msg1] Switch (o2) [in: msg1] @type='MATMAS05' Translation (o5) [in: msg1, out: msg2] @type='MATMAS04' Translation (o3) [in: msg1, out: msg2] Receive (o1) [service: s3, out: msg1] Switch (o2) [in: msg1] @type='MATMAS05' Translation (o5) [in: msg1, out: msg2] Assign (o4) [in: msg2, out: msg3] Assign (o6) [in: msg2, out: msg3] Assign (o4) [in: msg2, out: msg3] Assign (o6) [in: msg2, out: msg3] Invoke (o7) [service s1, in: msg3] Fork (o-1) Assign (o8) [in: msg2, out: msg4] Invoke (o9) [service s2, in: msg4] Invoke (o7) [service s1, in: msg3] Assign (o8) [in: msg2, out: msg4] Invoke (o9) [service s2, in: msg4] (a) Plan P 1 (b) Plan P ′ 1 @type='MATMAS05' Translation (o5) [in: msg1, out: msg2] Receive (o1) [service: s3, out: msg1] Switch (o2) [in: msg1] @type='MATMAS04' Translation (o3) [in: msg1, out: msg2] @type='MATMAS05' Translation (o5) [in: msg1, out: msg2] Receive (o1) [service: s3, out: msg1] Switch (o2) [in: msg1] @type='MATMAS04' Translation (o3) [in: msg1, out: msg2] Assign (o6) [in: msg2, out: msg3] Assign (o4) [in: msg2, out: msg3] Assign (o6) [in: msg2, out: msg3] Assign (o4) [in: msg2, out: msg3] Fork (o-1) Fork (o-1) Invoke (o7) [service s1, in: msg3] Assign (o8) [in: msg2, out: msg4] Assign (o8) [in: msg2, out: msg4] Invoke (o7) [service s1, in: msg3] Invoke (o9) [service s2, in: msg4] Invoke (o9) [service s2, in: msg4] (c) Plan P ′′ 1 (d) Plan P ′′′ 1 Figure 3.7: Example Execution of the Optimization Algorithm At operator o 1 we find no optimization technique. At operator o 2 , we recursively invoke the algorithm for the operator sequences (o 3 , o 4 ) and (o 5 , o 6 ) but do not find a different plan. Afterwards, the reordering of switch paths according to their cost-weighted path probabilities is applied, where the resulting plan P ′′ is shown in Figure 3.7(c). Further, we apply the rescheduling of parallel flows in order to start the most time-consuming flow first (Figure 3.7(d)). Note that the application order of different optimization techniques influences the resulting plan. For this reason, we predefined this application order for existing optimization techniques. Finally, we recursively invoke the algorithm for the sequences (o 7 ) and (o 8 , o 9 ) but do not find another plan. This overall cost-based optimization algorithm represents the framework for applying arbitrary optimization techniques. We describe selected techniques in Section 3.4 but one can easily extend the set of used techniques with additional ones. 3.3.2 Search Space Reduction Heuristics Our core optimization algorithm produces the globally optimal plan with regard to set of used optimization techniques and the monitored statistics. Additionally, we now provide heuristic optimization algorithms. Although these heuristics reduce the costs of pe- 50
3.3 Periodical Re-Optimization riodical re-optimization, we cannot guarantee to find the globally optimal plan. However, often good plans are produced by these heuristics. Essentially, we discuss the criticalPathOptimization algorithm (A-CPO) as well as the heuristicPatternMatchingOptimization algorithm (A-HPMO), which are both based on our standard A-PMO. Critical Path Optimization The application of our first heuristic, critical path optimization, promises optimization time reduction but does not change the asymptotic worst-case time complexity of the optimization algorithm. The critical path cp(P ) of the plan is determined in the form of operator execution times [LZ05]. We then apply the A-PMO only for this critical path and thus we might reduce the number of operators evaluated by the optimization algorithm from m to m ′ , where m ′ denotes the number of operators included in the critical path with m ′ = |o i ∈ cp(P )| and m ′ ≤ m. The intuition of this heuristic is that the execution time of a plan instance does not depend on parallel subflows, which execution time is subsumed by more time-consuming subflows. As a result, we only benefit from this heuristic if the plan includes a Fork operator. This heuristic exploits the control-flow semantics of our flow meta model. In the following, we illustrate the concept of critical path optimization with an example. Example 3.5 (Critical Path Optimization). Recall the plan P 3 ′ from Example 3.2. Clearly, the critical path of a plan can only be different to this plan if and only if parallel subflows (Fork operator) exist. Then, we determine the most time-consuming subflow of each Fork operator as part of the critical path, while all other subflows are subsumed and hence, are marked as not to be optimized. Figure 3.8 illustrates the determined critical path of the running example P 3 , where (o 3 ) is subsumed by (o 2 , o 5 ). Hence, we have reduced the number of operators that are included in the optimization. Assign (o1) [out: msg1] [5ms] Assign (o1) [out: msg1] [5ms] Fork (o-1) Fork (o-1) [60ms] Invoke (o2) [service s4, in: msg1, out: msg2] [35ms] Groupby (o5) [in: msg2, out: msg4] [26ms] Invoke (o3) [service s5, in: msg1, out: msg3] [60ms] Invoke (o2) [service s4, in: msg1, out: msg2] [35ms] Groupby (o5) [in: msg2, out: msg4] Join (o4) [in: msg4,msg3, out: msg5] [10.01ms] Join (o4) [in: msg4,msg3, out: msg5] [10.01ms] Assign (o6) [in: msg5, out: msg1] [5ms] Assign (o6) [in: msg5, out: msg1] [5ms] Invoke (o7) [service s5, in: msg1] [40ms] Invoke (o7) [service s5, in: msg1] [40ms] (a) Plan P ′ 3 (b) Plan cp(P ′ 3) Figure 3.8: Example Critical Path Optimization This algorithm is a heuristic because we cannot guarantee to find the globally optimal plan for two reasons. First, the critical path might change during the optimization. As a consequence of not considering certain operators of the new critical path, we might get suboptimal plans. Second, we do not consider data-flow-oriented operators not included 51
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Cost-Based Optimization of Integrat
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of traditional data management syst
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Contents 1 Introduction 1 2 Prelimi
Page 9: Contents 6.5 Experimental Evaluatio
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Page 14 and 15: 1 Introduction violated. We present
Page 16 and 17: 2 Preliminaries and Existing Techni
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4 Vectorizing Integration Flows (a)
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4 Vectorizing Integration Flows o 2
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4 Vectorizing Integration Flows In
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4 Vectorizing Integration Flows 2.
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4 Vectorizing Integration Flows The
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4 Vectorizing Integration Flows ord
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4 Vectorizing Integration Flows 4.3
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4 Vectorizing Integration Flows P
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4 Vectorizing Integration Flows P
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4 Vectorizing Integration Flows t1:
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4 Vectorizing Integration Flows We
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4 Vectorizing Integration Flows 4.7
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5 Multi-Flow Optimization cannot be
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5 Multi-Flow Optimization the query
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5 Multi-Flow Optimization The incom
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5 Multi-Flow Optimization example p
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5 Multi-Flow Optimization not allow
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5 Multi-Flow Optimization partition
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5 Multi-Flow Optimization mention i
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5 Multi-Flow Optimization • Case
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5 Multi-Flow Optimization k ′ . F
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5 Multi-Flow Optimization partition
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5 Multi-Flow Optimization the waiti
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5 Multi-Flow Optimization Execution
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5 Multi-Flow Optimization Thus, for
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5 Multi-Flow Optimization Thus, T L
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5 Multi-Flow Optimization • P 5 :
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5 Multi-Flow Optimization decreasin
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5 Multi-Flow Optimization (a) Fixed
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5 Multi-Flow Optimization reached,
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5 Multi-Flow Optimization (2) plan
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6 On-Demand Re-Optimization categor
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6 On-Demand Re-Optimization present
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6 On-Demand Re-Optimization stratum
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6 On-Demand Re-Optimization o 3 o 4
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6 On-Demand Re-Optimization For on-
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6 On-Demand Re-Optimization 6.3.1 O
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6 On-Demand Re-Optimization such th
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6 On-Demand Re-Optimization Join En
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6 On-Demand Re-Optimization f γ((
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6 On-Demand Re-Optimization The res
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6 On-Demand Re-Optimization project
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6 On-Demand Re-Optimization (a) Sel
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6 On-Demand Re-Optimization (a) Loa
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6 On-Demand Re-Optimization ical re
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6 On-Demand Re-Optimization evaluat
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6 On-Demand Re-Optimization 6.6 Sum
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7 Conclusions Existing approaches b
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Bibliography [BBD05a] Shivnath Babu
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Bibliography [BHP + 09b] [BHP + 11]
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Bibliography [CM95] Sophie Cluet an
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Bibliography [GZ08] [HA03] [Haa07]
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Bibliography [IKNG09] [INSS92] [Ioa
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Bibliography [LX09] [LZ05] Rubao Le
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Bibliography [OMG07] OMG. XML Metad
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Bibliography [Sto02] Michael Stoneb
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Bibliography [ZRH04] Yali Zhu, Elke
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List of Figures 3.27 Workload Adapt
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List of Tables 2.1 Interaction-Orie
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Selbstständigkeitserklärung Hierm
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