Results 1 -
5 of
5
Dynamic decision networks for decision-making in self-adaptive systems: A case study
- in Proceedings of the 8th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, ser. SEAMS ’13. Piscataway
, 2013
"... Abstract—Bayesian decision theory is increasingly applied to support decision-making processes under environmental vari-ability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering ..."
Abstract
-
Cited by 6 (3 self)
- Add to MetaCart
(Show Context)
Abstract—Bayesian decision theory is increasingly applied to support decision-making processes under environmental vari-ability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. However, in the area of software engineering and specifically in the area of self-adaptive systems (SASs), little progress has been made in the application of Bayesian decision theory. We believe that techniques based on Bayesian Networks (BNs) are useful for systems that dynamically adapt themselves at runtime to a changing environment, which is usually uncer-tain. In this paper, we discuss the case for the use of BNs, specifically Dynamic Decision Networks (DDNs), to support the decision-making of self-adaptive systems. We present how such a probabilistic model can be used to support the decision-making in SASs and justify its applicability. We have applied our DDN-based approach to the case of an adaptive remote data mirroring system. We discuss results, implications and potential benefits of the DDN to enhance the development and operation of self-adaptive systems, by providing mechanisms to cope with uncertainty and automatically make the best decision. Index Terms—self-adaptive systems, dynamic decision net-works, bayesian networks, uncertainty modeling. I.
Minimizing Nasty Surprises with Better Informed Decision-Making in Self-Adaptive Systems
"... Abstract—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 characterize ..."
Abstract
-
Cited by 3 (2 self)
- Add to MetaCart
(Show Context)
Abstract—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. I.
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 ..."
Abstract
- Add to MetaCart
(Show Context)
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.
Requirements-aware Systems for Self-adaptation under Uncertainty Research Statement
"... The development of software-intensive systems is driven by their requirements. Traditional requirements engineering (RE) methods focus on resolving ambiguities in requirements and advocate specifying require-ments in sufficient detail so that the implementation can be checked against them for confor ..."
Abstract
- Add to MetaCart
(Show Context)
The development of software-intensive systems is driven by their requirements. Traditional requirements engineering (RE) methods focus on resolving ambiguities in requirements and advocate specifying require-ments in sufficient detail so that the implementation can be checked against them for conformance. In an ideal world, this way of thinking can be very effective. Requirements can be specified clearly, updated as necessary, and evolutions of the software design can be made with the requirements in mind. Increasingly, however, it is not sufficient to fix requirements statically because they will change at runtime as the operating environment changes. Furthermore, as software systems become more pervasive, there is growing uncertainty about the environment and so requirements changes cannot be predicted at design-time [12, 24, 39, 1, 20]. It is considerations such as these that have led to the development of self-adaptive systems (SASs) [11], which have the ability to dynamically and autonomously reconfigure their behavior to respond to changing external conditions. Consider a scenario involving a robot vacuum cleaner for domestic apartments. The vacuum cleaner has goals clean apartment, avoid tripping hazard and minimize energy costs. Further, it has the domain assumption energy is cheapest at night. To satisfy the avoid tripping hazard goal, a requirement is derived that it should stop operating as soon as any human activity is detected. Night operation satisfies the
EAI Endorsed Transactions on Self-Adaptive Systems Research Article 1 “Why can’t I do that?”: Tracing Adaptive Security Decisions
"... One of the challenges of any adaptive system is to ensure that users can understand how and why the behaviour of the system changes at runtime. This is particularly important for adaptive security behaviours which are essential for applications that are used in many different contexts, such as those ..."
Abstract
- Add to MetaCart
(Show Context)
One of the challenges of any adaptive system is to ensure that users can understand how and why the behaviour of the system changes at runtime. This is particularly important for adaptive security behaviours which are essential for applications that are used in many different contexts, such as those hosted in the cloud. In this paper, we propose an approach for using traceability information, enriched with causality relations and contextual attributes of the deployment environment, when providing feedback to the users. We demonstrate, using a cloud storage-as-a-service environment, how our approach provides users of cloud applications better information, explanations and assurances about the security decisions made by the system. This enables the user to understand why a certain security adaptation has occurred, how the adaptation is related to current context of use of the application, and a guarantee that the application still satisfies its security requirements after an adaptation.
