Abstract: The problem of mitigating uncertainty in self-adaptation has driven much of the research proposed in the area of software engineering for self-adaptive systems in the last decade. Although many solutions have already been proposed, most of them tend to tackle specific types, sources, and dimensions of uncertainty (e.g., in goals, resources, adaptation functions) in isolation. A special concern are the aspects associated with uncertainty modeling in an integrated fashion. Different uncertainties are rarely independent and often compound, affecting the satisfaction of goals and other system properties in subtle and often unpredictable ways. Hence, there is still limited understanding about the specific ways in which uncertainties from various sources interact and ultimately affect the properties of self-adaptive, software-intensive systems. In this SoSym expert voice, we introduce the Uncertainty Interaction Problem as a way to better qualify the scope of the challenges with respect to representing different types of uncertainty while capturing their interaction in models employed to reason about self-adaptation. We contribute a characterization of the problem and discuss its relevance in the context of case studies taken from two representative application domains. We posit that the Uncertainty Interaction Problem should drive future research in software engineering for autonomous and self-adaptive systems, and therefore, contribute to evolving uncertainty modeling towards holistic approaches that would enable the construction of more resilient self-adaptive systems.

Abstract: Requirements for high availability in computing systems today demand that systems be self-adaptive to maintain expected qualities-of-service in the presence of system faults, variable environmental conditions, and changing user requirements. Autonomic computing tackles the challenge of automating tasks that humans would otherwise have to perform to achieve this goal. However, existing approaches to autonomic computing lack the ability to capture routine human repair tasks in a way that takes into account the business context that humans use in selecting an appropriate form of adaptation, while dealing with timing delays and uncertainties in outcome of repair actions. In this article, we present Stitch, a language for representing repair strategies within the context of an architecture-based self-adaptation framework. Stitch supports the explicit representation of repair decision trees together with the ability to express business objectives, allowing a self-adaptive system to select a strategy that has optimal utility in a given context, even

Abstract: This paper presents an extension of existing software architecture tools to model physical systems, their interconnections, and the interactions between physical and cyber components. A new CPS architectural style is introduced to support the principled design and evaluation of alternative architectures for cyber-physical systems (CPSs). The implementation of the CPS architectural style in AcmeStudio includes behavioral annotations on components and connectors using either finite state processes (FSP) or linear hybrid automata (LHA) with plug-ins to perform behavior analysis using the Labeled Transition System Analyzer (LTSA) or Polyhedral Hybrid Automata Verifier (PHAVer), respectively. The CPS architectural style and analysis plug-ins are illustrated with an example.

Abstract: The Rainbow framework uses software architectures and a reusable infrastructure to support self-adaptation of software systems. The use of external adaptation mechanisms allows the explicit specification of adaptation strategies for multiple system concerns.

Abstract: Complex software systems require expressive notations for representing their software architectures. Two competing paths have emerged. One is to use a specialized notation for architecture - or architecture description language (ADL). The other is to adapt a general-purpose modeling notation, such as UML. The latter has a number of benefits, including familiarity to developers, close mapping to implementations, and commercial tool support. However, it remains an open question as to how best to use object-oriented notations for architectural description, and, indeed, whether they are sufficiently expressive, as currently defined. In this paper we take a systematic look at these questions, examining the space of possible mappings from ADLs into object notations. Specifically, we describe (a) the principle strategies for representing architectural structure in UML; (b) the benefits and limitations of each strategy; and (c) aspects of architectural description that are intrinsically difficult to model in UML using the strategies.

Abstract: Increasingly, systems must have the ability to self-adapt to meet changes in their execution environment. Unfortunately, existing solutions require human oversight, or are limited in the kinds of systems and the set of quality-of-service concerns they address. Our approach, embodied in a system called Rainbow, uses software architecture models and architectural styles to overcome existing limitations. It provides an engineering approach and a framework of mechanisms to monitor a target system and its environment, reflect observations into a system's architecture model, detect opportunities for improvements, select a course of action, and effect changes in a closed loop. The framework provides general and reusable infrastructures with well-defined customization points, allowing engineers to systematically customize Rainbow to particular systems and concerns.

Abstract: An increasingly important concern for modern systems design is how best to incorporate self-adaptation into systems so as to improve their ability to dynamically respond to faults, resource variation, and changing user needs. One promising approach is to use architectural models as a basis for monitoring, problem detection, and repair selection. While this approach has been shown to yield positive results, current systems use a reactive approach: they respond to problems only when they occur. In this paper we argue that self-adaptation can be improved by adopting an anticipatory approach in which predictions are used to inform adaptation strategies. We show how such an approach can be incorporated into an architecture-based adaptation framework and demonstrate the benefits of the approach.

Abstract: One increasingly important technique for improving system dependability is to provide mechanisms for a system to adapt at run time in order to accommodate varying resources, system errors, and changing requirements. For such 'self-repairing' systems one of the hard problems is determining when a change is needed, and knowing what kind of adaptation is required. In this paper we describe a partial solution in which stylized architectural design models are maintained at run time as a vehicle for automatically monitoring system behavior, for detecting when that behavior falls outside of acceptable ranges, and for deciding on a high-level repair strategy. The main innovative feature of the approach is the ability to specialize a generic run time adaptation framework to support particular architectural styles and properties of interest. Specifically, a formal description of an architectural style defines for a family of related systems the conditions under which adaptation should be considered, provides an analytic basis for detecting anomalies, and serves as a basis for developing sound repair strategies.

Abstract: As software systems become increasingly autonomous and capable of synthesizing their own adaptive strategies, embedding ethical reasoning into self-adaptive controllers raises fundamental ques- tions about trust, responsibility, and moral agency. When adapta- tion unfolds under uncertainty, system decisions inevitably reflect implicit values originating from design choices, training data, and operational contexts. This challenges traditional assumptions about where ethical judgment resides and whether ethics-aware systems should follow fixed moral principles or dynamically negotiate ethi- cal trade-offs at runtime. This paper presents a collective contribution from a commu- nity debate panel of four eminent researchers at the 21st Interna- tional Conference on Software Engineering for Adaptive and Self- Managing Systems (SEAMS), each offering a distinct perspective on the trustworthiness, challenges, implications, and opportunities shaping the emerging horizon of ethics-aware controllers.

Abstract: This paper argues that change alone is sufficiently difficult; simply, handling change, i.e., anticipated changes, remains the ultimate challenge of self-adaptation.

Abstract: Many software systems operate in environments where change and uncertainty are the rule, rather than exceptions. Techniques for self-adaptation allow these systems to automatically respond to environmental changes, yet they do not handle changes to the adaptive system itself, such as the addition or removal of adaptation tactics. Instead, changes in a self-adaptive system often require a human planner to redo an expensive planning process to allow the system to continue satisfying its quality requirements under different conditions; automated techniques typically must replan from scratch. We propose to address this problem by reusing prior planning knowledge to adapt in the face of unexpected situations. We present a planner based on genetic programming that reuses existing plans. While reuse of material in genetic algorithms has recently applied successfully in the area of automated program repair, we find that naïvely reusing existing plans for self-* planning actually results in a loss of utility. Furthermore, we propose a series of techniques to lower the costs of reuse, allowing genetic techniques to leverage existing information to improve planning utility when replanning for unexpected changes.

Abstract: One common approach to self-adaptive systems is to incorporate a control layer that monitors a system, supervisorily detects problems, and applies adaptation strategies to fix problems or improve system behavior. While such approaches have been found to be quite effective, they are typically limited to carrying out a single adaptation at a time, delaying other adaptations until the current one finishes. This in turn leads to a problem in which a time-critical adaptation may have to wait for an existing long-running adaptation to complete, thereby missing a window of opportunity for that adaptation. In this paper we improve on existing practice through an approach in which adaptations can be preempted to allow for other time-critical adaptations to be scheduled. Scheduling is based on an algorithm that maximizes time-related utility for a set of concurrently executing adaptations.

Abstract: Rainbow is an approach for engineering self-adaptive systems, with run-time, closed-loop control over target systems to monitor, detect, decide, and act on opportunities for system improvement. RAIDE enables adaptation engineers to customize the Rainbow framework, simulate adaptation behavior, and deploy Rainbow run-time components.

Abstract: Autonomic, or self-adaptive, systems are increasingly important. One of the most prevalent techniques is to adopt a control systems view of the solution: adding a runtime, separate control unit that monitors and adapts the system under consideration. A problem with this paradigm for system engineering is that the control and the system are loosely coupled, introducing a variety of sources of uncertainty. In this paper we describe three specific sources of uncertainty, and briefly explain how we address those in the Rainbow Project.

Abstract: In the world of autonomic computing, the ultimate aim is to automate human tasks in system management to achieve high-level stakeholder objectives. One common approach is to capture and represent human expertise in a form executable by a computer. Techniques to capture such expertise in programs, scripts, or rule sets are effective to an extent. However, they are often incapable of expressing the necessary adaptation expertise and emulating the subtleties of trade-offs in high-level decision making. In this paper, we propose a new language of adaptation that is suffi-ciently expressive to capture the subtleties of choice, deriving its ontology from system administration tasks and its underlying formalism from utility theory.

Abstract: In this paper, we envision a world where a software engineer could take an existing software system, specify, for a set of properties of interest, an objective, conditions for change, and strategies for their adaptation and, within a few man weeks, make that system self-adaptive where it was not before. We describe how our approach generalizes to different classes of systems and holds promise for cost-effective, dynamic system self-adaptation to become an engineering reality.

Abstract: A common approach to adding self-management capabilities to a system is to provide one or more external control modules, whose responsibility is to monitor system behavior, and adapt the system at run time to achieve various goals (configure the system, improve performance, recover from faults, etc.). An important problem arises when there is more than one such self-management module: how can one make sure that they are composed to provide consistent and complementary benefits? In this paper we describe a solution that introduces a self-management coordination architecture and infrastructure to support such composition. We focus on the problem of coordinating self-configuring and self-healing capabilities, particularly with respect to global configuration and incremental repair. We illustrate the approach in the context of a self-managing video teleconference system that composes two pre-existing adaptation modules to achieve synergistic benefits of both.

Abstract: An increasingly important requirement for software systems is the capability to adapt at run time in order to accommodate varying resources, system errors, and changing requirements. For such self-repairing systems, one of the hard problems is determining when a change is needed, and knowing what kind of adaptation is required. Recently several researchers have explored the possibility of using architectural models as a basis for run time monitoring, error detection, and repair. Each of these efforts, however, has demonstrated the feasibility of using architectural models in the context of a specific style. In this paper we show how to generalize these solutions by making architectural style a parameter in the monitoring/repair framework and its supporting infrastructure. The value of this generalization is that it allows one to tailor monitoring/ repair mechanisms to match both the properties of interest (such as performance or security), and the available operators for run time adaptation.

Abstract: Grid applications must increasingly self-adapt dynamically to changing environments. In most cases, adaptation has been implemented in an ad hoc fashion, on a perapplication basis. This paper describes work which generalizes adaptation so that it can be used across applications by providing an adaptation framework. This framework uses a software architectural model of the system to analyze whether the application requires adaptation, and allows repairs to be written in the context of the architectural model and propagated to the running system. In this paper, we exemplify our framework by applying it to the domain of load-balancing a client-server system. We report on an experiment conducted using our framework, which illustrates that this approach maintains architectural requirements.

Abstract: An important requirement for pervasive computing systems is the ability to adapt at runtime to handle varying resources, user mobility, changing user needs, and system faults. In this paper we describe an approach in which dynamic adaptation is supported by the use of software architectural models to monitor an application and guide dynamic changes to it. The use of externalized models permits one to make reconfiguration decisions based on a global per-spective of the running system, apply analytic models to determine correct re-pair strategies, and gauge the effectiveness of repair through continuous system monitoring. We illustrate the application of this idea to pervasive computing systems, focusing on the need to adapt based on performance-related criteria and models.

Abstract: Complex software systems require expressive notations for representing their software architectures. Two competing paths have emerged, one using a specialized notation for architecture - or architecture description language (ADL), the other using notations applied generally throughout design, such as UML. The latter has a number of benefits, including familiarity to developers, close mappings to implementations, and commercial tool support. However, it remains an open question how best to use object-oriented notations for architectural description and whether they are sufficiently expressive as currently defined. In this paper, we present a mapping between Acme?a notation designed for expressing architectures - and the UML Real-Time Profile - an object-oriented design notation. Specifically, we describe (a) how to map Acme descriptions to descriptions in the UML Real-Time Profile, and (b)the places where this mapping breaks down.

Abstract: Researchers, practitioners, educators, and students of software architecture would benefit from having online access to quality information about the state of research and practice of software architecture. In recent years, Wiki technology has enabled distributed and collaborative editing of content using only a Web browser. To explore whether Wiki technology would be effective in facilitating the ongoing discussion and evolution of ideas on software architecture, we hosted the WICSA Wiki WAN Party (WWWP) during the 4th Working IEEE/IFIP Conference on Software Architecture (WICSA 2004). We used a history tool developed at IBM Research to monitor site activity and provide daily feedback to conference participants. This report recounts experience hosting this Wiki site and summarizes the site activity.

Abstract: Modern, complex software systems (e-commerce, IT, critical infrastructures, etc.) are increasingly required to continue operation in the face of change to self-adapt to accommodate shifting user priorities, resource variability, changing environments, and component failures. While manual oversight benefits from global problem contexts and flexible policies, human operators are costly and prone to error. Low-level, embedded mechanisms (exceptions, time-outs, etc.) are effective and timely for error recovery, but are local in scope to the point-of-failure, application-specific, and costly to modify when adaptation objectives change. An ideal solution leverages domain expertise, provides an end-to-end system perspective, adapts the target system in a timely manner, and can be engineered cost-effectively. Architecture-based self-adaptation closes the "loop of control," using external mechanisms and the architecture model of the target system to adapt the system. An architecture model exposes important system properties and constraints, provides end-to-end problem contexts, and allows principled and automated adaptations. Existing architecture-based approaches specialize support for particular classes of systems and fixed sets of quality-of-service concerns; they are costly to develop for new systems and to evolve for new qualities. To overcome these limitations, we pose this thesis: We can provide software engineers the ability to add and evolve self-adaptation capabilities cost-effectively, for a wide range of software systems, and for multiple objectives, by defining a self-adaptation framework that factors out common adaptation mechanisms and provides explicit customization points to tailor self-adaptation capabilities for particular classes of systems, for multiple quality-of-service objectives. Our approach, embodied in a system called Rainbow, provides an engineering approach and a framework of mechanisms to monitor a target system and its environment, reflect observations into the system's architecture model, detect opportunities for improvements, select a course of action, and effect changes. The framework provides general and reusable infrastructures with well-defined customization points, a set of abstractions, and an adaptation engineering process, focusing engineers on adaptation concerns to systematically customize Rainbow to particular systems. To automate system self-adaptation, Rainbow provides a language, called Stitch, to represent routine human adaptation knowledge using a core set of adaptation concepts.