Decision-making for self-adaptation approaches need to address different challenges, including the quantification of the uncertainty of events that cannot be foreseen in advance and their effects, and dealing with conflicting objectives that inherently involve multi-objective decision making (e.g., avoiding costs vs. providing reliable service). To enable researchers to evaluate and compare decision-making techniques for self-adaptation, we present the RDMSim exemplar. RDMSim enables researchers to evaluate and compare techniques for decision-making under environmental uncertainty that support self-adaptation. The focus of the exemplar is on the domain problem related to Remote Data Mirroring, which gives opportunity to face the challenges described above. RDMSim provides probe and effector components for easy integration with external adaptation managers, which are associated with decision-making techniques and based on the MAPE-K loop. Specifically, the paper presents (i) RDMSim, a simulator for real-world experimentation, (ii) a set of realistic simulation scenarios that can be used for experimentation and comparison purposes, (iii) data for the sake of comparison.
|Title of host publication||Proceedings - 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021|
|Number of pages||7|
|Publication status||Published - 29 Jun 2021|
|Event||2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) - Madrid, Spain|
Duration: 18 May 2021 → 24 May 2021
|Name||Proceedings - 2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems, SEAMS 2021|
|Conference||2021 International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS)|
|Period||18/05/21 → 24/05/21|
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Funding: This work has been partially supported by The Leverhulme Trust Fellowship ”QuantUn: quantification of uncertainty using Bayesian surprises” (Grant No. RF-2019-548/9) and the EPSRC Research Project Twenty20Insight (Grant No.
- Remote Data Mirroring
- Self-Adaptive System