RDMSim: An Exemplar for Evaluation and Comparison of Decision-Making Techniques for Self-Adaptation

TL;DR

RDMSim is a simulator designed to evaluate and compare decision-making techniques for self-adaptation in uncertain environments, focusing on remote data mirroring and supporting multi-objective optimization.

Contribution

The paper introduces RDMSim, a comprehensive simulation framework with scenarios and data for assessing decision-making methods in self-adaptive systems under uncertainty.

Findings

Provides a versatile simulation environment for experimentation

Enables comparison of decision-making techniques in realistic scenarios

Supports evaluation of multi-objective decision strategies

Abstract

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.