RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation
This provides a tool for researchers in self-adaptive systems to benchmark decision-making approaches, but it is incremental as it focuses on creating a simulation environment rather than advancing the techniques themselves.
The paper tackles the challenge of evaluating and comparing decision-making techniques for self-adaptation by presenting RDMSim, an exemplar that enables researchers to assess methods under environmental uncertainty and conflicting objectives in the Remote Data Mirroring domain.
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.