Decision-making under uncertainty: be aware of your priorities

Abstract

Self-adaptive systems (SASs) are increasingly leveraging autonomy in their decision-making to manage uncertainty in their operating environments. A key problem with SASs is ensuring their requirements remain satisfied as they adapt. The trade-off analysis of the non-functional requirements (NFRs) is key to establish balance among them. Further, when performing the trade-offs it is necessary to know the importance of each NFR to be able to resolve conflicts among them. Such trade-off analyses are often built upon optimisation methods, including decision analysis and utility theory. A problem with these techniques is that they use a single-scalar utility value to represent the overall combined priority for all the NFRs. However, this combined scalar priority value may hide information about the impacts of the environmental contexts on the individual NFRs’ priorities, which may change over time. Hence, there is a need for support for runtime, autonomous reasoning about the separate priority values for each NFR, while using the knowledge acquired based on evidence collected. In this paper, we propose Pri-AwaRE, a self-adaptive architecture that makes use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to perform decision-making for SASs while offering awareness of NFRs’ priorities. MR-POMDP is used as a priority-aware runtime specification model to support runtime reasoning and autonomous tuning of the distinct priority values of NFRs using a vector-valued reward function. We also evaluate the usefulness of our Pri-AwaRE approach by applying it to two substantial example applications from the networking and IoT domains.

Explaining Quality Attribute Tradeoffs in Automated Planning for Self-Adaptive Systems

“…In our examples, we performed uniform sampling of different combinations of utility function weights, but other sampling strategies are applicable as well. Instead of utility functions, for instance, priority values for non-functional requirements (Samin et al, 2022) can be used to capture the priorities of different quality attributes. The dataframe should have a sufficiently large number of samples to apply the machine learning algorithms (i.e., no less than 200).…”

The uncertainty interaction problem in self-adaptive systems

“…it depends on sensor values, which are imprecise. Suppose as well that the utility functions, which define the objectives, are neither precisely defined because stakeholders do not have a precise idea about in what contexts they should prioritize performance over safety (the information can be learned during run-time [3,40]). Similarly, the decision to change a resource may be much more difficult when it is not clear if the resource is available or not, and such a decision is based on imprecise functions.…”

Engineering Self-Adaptive Capabilities in Wireless Sensors Based on Control Algorithms and Model-Based Design Methodologies

“…Self-adaptation issues in embedded software have been investigated across different areas of software engineering, such as requirements engineering [1][2][3][4], software architecture [5][6][7][8][9][10], middleware architectures [11][12][13][14], component-based development [15][16][17], model-driven development [18][19][20][21] and goal-driven models [22][23][24][25]. The majority of these works have been isolated initiatives.…”