Minimizing nasty surprises with better informed decision-making in self-adaptive systems

Designers of self-adaptive systems often formulate adaptive design decisions, making unrealistic or myopic assumptions about the system's requirements and environment. The decisions taken during this formulation are crucial for satisfying requirements. In environments which are characterized by uncertainty and dynamism, deviation from these assumptions is the norm and may trigger 'surprises'. Our method allows designers to make explicit links between the possible emergence of surprises, risks and design trade-offs. The method can be used to explore the design decisions for self-adaptive systems and choose among decisions that better fulfil (or rather partially fulfil) non-functional requirements and address their trade-offs. The analysis can also provide designers with valuable input for refining the adaptation decisions to balance, for example, resilience (i.e. Satisfiability of non-functional requirements and their trade-offs) and stability (i.e. Minimizing the frequency of adaptation). The objective is to provide designers of self adaptive systems with a basis for multi-dimensional what-if analysis to revise and improve the understanding of the environment and its effect on non-functional requirements and thereafter decision-making. We have applied the method to a wireless sensor network for flood prediction. The application shows that the method gives rise to questions that were not explicitly asked before at design-time and assists designers in the process of risk-aware, what-if and trade-off analysis

Decision-Making under Uncertainty: Be Aware of your Priorities

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

Runtime models based on dynamic decision networks:enhancing the decision-making in the domain of ambient assisted living applications

Dynamic decision-making for self-Adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -Aka quality properties-And the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-Time. In this paper we develop further our ideas on re-Assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage

The Reassessment of Preferences of Non-Functional Requirements for Better Informed Decision-making in Self-Adaptation

Decision-making requires the quantification and trade-off of multiple non-functional requirements (NFRs) and the analysis of costs and benefits between alternative solutions. Different techniques have been used to specify utility preferences for NFRs and decision-making strategies of self-adaptive systems (SAS). These preferences are defined during design-time. It is well known that correctly identifying the weight of the NFRs is a major difficulty. In this paper we present initial results of a novel approach that provides a set of criteria to re-assess NFRs preferences given new evidence found at runtime using dynamic decision networks (DDNs). The approach use both conditional probabilities provided by DDNs and the concept of Bayesian surprise. The results show that our approach supports better informed decisions under uncertainty by identifying new situations where the current SAS preferences may need to be re-evaluated to improve the levels of satisfaction of NFRs

Runtime Models Based on Dynamic Decision Networks: Enhancing the Decision-making in the Domain of Ambient Assisted Living Applications

Abstract-Dynamic decision-making for self-adaptive systems (SAS) requires the runtime trade-off of multiple non-functional requirements (NFRs) -aka quality properties-and the costsbenefits analysis of the alternative solutions. Usually, it requires the specification of utility preferences for NFRs and decisionmaking strategies. Traditionally, these preferences have been defined at design-time. In this paper we develop further our ideas on re-assessment of NFRs preferences given new evidence found at runtime and using dynamic decision networks (DDNs) as the runtime abstractions. Our approach use conditional probabilities provided by DDNs, the concepts of Bayesian surprise and Primitive Cognitive Network Process (P-CNP), for the determination of the initial preferences. Specifically, we present a case study in the domain problem of ambient assisted living (AAL). Based on the collection of runtime evidence, our approach allows the identification of unknown situations at the design stage