Integration flows are used to propagate data between heterogeneous operational systems or to consolidate data into data warehouse infrastructures. In order to meet the increasing need of up-to-date information, many messages are exchanged over time. The efficiency of those integration flows is therefore crucial to handle the high load of messages and to reduce message latency. State-of-the-art strategies to address this performance bottleneck are based on incremental statistic maintenance and periodic cost-based re-optimization. This also achieves adaptation to unknown statistics and changing workload characteristics, which is important since integration flows are deployed for long time horizons. However, the major drawbacks of periodic re-optimization are many unnecessary re-optimization steps and missed optimization opportunities due to adaptation delays. In this paper, we therefore propose the novel concept of on-demand re-optimization. We exploit optimality conditions from the optimizer in order to (1) monitor optimality of the current plan, and (2) trigger directed re-optimization only if necessary. Furthermore, we introduce the PlanOptimalityTree as a compact representation of optimality conditions that enables efficient monitoring and exploitation of these conditions. As a result and in contrast to existing work, re-optimization is immediately triggered but only if a new plan is certain to be found. Our experiments show that we achieve near-optimal re-optimization overhead and fast workload adaptation.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:86380 |
| Date | 04 July 2023 |
| Creators | Böhm, Matthias, Habich, Dirk, Lehner, Wolfgang |
| Publisher | Elsevier |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/acceptedVersion, doc-type:article, info:eu-repo/semantics/article, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | 0306-4379, 10.1016/j.is.2014.03.005 |
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