Danny Weyns

Software Engineering for Self-Adaptive Systems: A Second Research Roadmap

The goal of this roadmap paper is to summarize the state-of-the-art and identify research challenges when developing, deploying and managing self-adaptive software systems. Instead of dealing with a wide range of topics associated with the field, we focus on four essential topics of self-adaptation: design space for self-adaptive solutions, software engineering processes for self-adaptive systems, from centralized to decentralized control, and practical run-time verification & validation for self-adaptive systems. For each topic, we present an overview, suggest future directions, and focus on selected challenges. This paper complements and extends a previous roadmap on software engineering for self-adaptive systems published in 2009 covering a different set of topics, and reflecting in part on the previous paper. This roadmap is one of the many results of the Dagstuhl Seminar 10431 on Software Engineering for Self-Adaptive Systems, which took place in October 2010.

Environment as a first class abstraction in multiagent systems

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems. In this paper, we put forward the environment as an explicit part of multiagent systems.We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.

Environments for Multi-Agent Systems III

Models, Architecture, and Design.- A Reference Architecture for Situated Multiagent Systems.- A Unified Model for Physical and Social Environments.- Exploiting the Environment for Coordinating Agent Intentions.- CArtA gO: A Framework for Prototyping Artifact-Based Environments in MAS.- Mediated Interaction and Stigmery.- Environment as Active Support of Interaction.- Environmental Support for Tag Interactions.- Cognitive Stigmergy: Towards a Framework Based on Agents and Artifacts.- Trace Signals: The Meanings of Stigmergy.- Regulation Function of the Environment in Agent-Based Simulation.- Governing Environment.- Establishing Global Properties of Multi-Agent Systems Via Local Laws.- E4MAS Through Electronic Institutions.- Spatially Distributed Normative Infrastructure.- Enhancing the Environment with a Law-Governed Service for Monitoring and Enforcing Behavior in Open Multi-Agent Systems.- Applications.- Urban Traffic Control with Co-Fields.- Designing Self-organising MAS Environments: The Collective Sort Case.

On Patterns for Decentralized Control in Self-Adaptive Systems

Self-adaptation is typically realized using a control loop. One prominent approach for organizing a control loop in self-adaptive systems is by means of four components that are responsible for the primary functions of self-adaptation: Monitor, Analyze, Plan, and Execute, together forming a MAPE loop. When systems are large, complex, and heterogeneous, a single MAPE loop may not be sufficient for managing all adaptation in a system, so multiple MAPE loops may be introduced. In self-adaptive systems with multiple MAPE loops, decisions about how to decentralize each of the MAPE functions must be made. These decisions involve how and whether the corresponding functions from multiple loops are to be coordinated (e.g., planning components coordinating to prepare a plan for an adaptation). To foster comprehension of self-adaptive systems with multiple MAPE loops and support reuse of known solutions, it is crucial that we document common design approaches for engineers. As such systematic knowledge is currently lacking, it is timely to reflect on these systems to: (a) consolidate the knowledge in this area, and (b) to develop a systematic approach for describing different types of control in self-adaptive systems. We contribute with a simple notation for describing interacting MAPE loops, which we believe helps in achieving (b), and we use this notation to describe a number of existing patterns of interacting MAPE loops, to begin to fulfill (a). From our study, we outline numerous remaining research challenges in this area.

A Decentralized Approach for Anticipatory Vehicle Routing Using Delegate Multiagent Systems

Advanced vehicle guidance systems use real-time traffic information to route traffic and to avoid congestion. Unfortunately, these systems can only react upon the presence of traffic jams and not to prevent the creation of unnecessary congestion. Anticipatory vehicle routing is promising in that respect, because this approach allows directing vehicle routing by accounting for traffic forecast information. This paper presents a decentralized approach for anticipatory vehicle routing that is particularly useful in large-scale dynamic environments. The approach is based on delegate multiagent systems, i.e., an environment-centric coordination mechanism that is, in part, inspired by ant behavior. Antlike agents explore the environment on behalf of vehicles and detect a congestion forecast, allowing vehicles to reroute. The approach is explained in depth and is evaluated by comparison with three alternative routing strategies. The experiments are done in simulation of a real-world traffic environment. The experiments indicate a considerable performance gain compared with the most advanced strategy under test, i.e., a traffic-message-channel-based routing strategy.

Multi-Agent Systems: Simulation and Applications

Methodological Guidelines for Modeling and Developing MAS-Based Simulations The intersection of agents, modeling, simulation, and application domains has been the subject of active research for over two decades. Although agents and simulation have been used effectively in a variety of application domains, much of the supporting research remains scattered in the literature, too often leaving scientists to develop multi-agent system (MAS) models and simulations from scratch. Multi-Agent Systems: Simulation and Applications provides an overdue review of the wide ranging facets of MAS simulation, including methodological and application-oriented guidelines. This comprehensive resource reviews two decades of research in the intersection of MAS, simulation, and different application domains. It provides scientists and developers with disciplined engineering approaches to modeling and developing MAS-based simulations. After providing an overview of the fields history and its basic principles, as well as cataloging the various simulation engines for MAS, the book devotes three sections to current and emerging approaches and applications. Simulation for MAS explains simulation support for agent decision making, the use of simulation for the design of self-organizing systems, the role of software architecture in simulating MAS, and the use of simulation for studying learning and stigmergic interaction. MAS for Simulation discusses an agent-based framework for symbiotic simulation, the use of country databases and expert systems for agent-based modeling of social systems, crowd-behavior modeling, agent-based modeling and simulation of adult stem cells, and agents for traffic simulation. Tools presents a number of representative platforms and tools for MAS and simulation, including Jason, James II, SeSAm, and RoboCup Rescue. Complete with over 200 figures and formulas, this reference book provides the necessary overview of experiences with MAS simulation and the tools needed to exploit simulation in MAS for future research in a vast array of applications including home security, computational systems biology, and traffic management.

FORMS: Unifying reference model for formal specification of distributed self-adaptive systems

The challenges of pervasive and mobile computing environments, which are highly dynamic and unpredictable, have motivated the development of self-adaptive software systems. Although noteworthy successes have been achieved on many fronts, the construction of such systems remains significantly more challenging than traditional systems. We argue this is partially because researchers and practitioners have been struggling with the lack of a precise vocabulary for describing and reasoning about the key architectural characteristics of self-adaptive systems. Further exacerbating the situation is the fact that existing frameworks and guidelines do not provide an encompassing perspective of the different types of concerns in this setting. In this article, we present a comprehensive reference model, entitled FOrmal Reference Model for Self-adaptation (FORMS), that targets both issues. FORMS provides rigor in the manner such systems can be described and reasoned about. It consists of a small number of formally specified modeling elements that correspond to the key concerns in the design of self-adaptive software systems, and a set of relationships that guide their composition. We demonstrate FORMSs ability to precisely describe and reason about the architectural characteristics of distributed self-adaptive software systems through its application to several existing systems. FORMSs expressive power gives it a potential for documenting reusable architectural solutions (e.g., architectural patterns) to commonly encountered problems in this area.

Variability in Software Systems—A Systematic Literature Review

Context: Variability (i.e., the ability of software systems or artifacts to be adjusted for different contexts) became a key property of many systems. Objective: We analyze existing research on variability in software systems. We investigate variability handling in major software engineering phases (e.g., requirements engineering, architecting). Method: We performed a systematic literature review. A manual search covered 13 premium software engineering journals and 18 premium conferences, resulting in 15,430 papers searched and 196 papers considered for analysis. To improve reliability and to increase reproducibility, we complemented the manual search with a targeted automated search. Results: Software quality attributes have not received much attention in the context of variability. Variability is studied in all software engineering phases, but testing is underrepresented. Data to motivate the applicability of current approaches are often insufficient; research designs are vaguely described. Conclusions: Based on our findings we propose dimensions of variability in software engineering. This empirically grounded classification provides a step towards a unifying, integrated perspective of variability in software systems, spanning across disparate or loosely coupled research themes in the software engineering community. Finally, we provide recommendations to bridge the gap between research and practice and point to opportunities for future research.

A survey of formal methods in self-adaptive systems

One major challenge in self-adaptive systems is to assure the required quality properties. Formal methods provide the means to rigorously specify and reason about the behaviors of self-adaptive systems, both at design time and runtime. To the best of our knowledge, no systematic study has been performed on the use of formal methods in self-adaptive systems. As a result, there is no clear view on what methods have been used to verify self-adaptive systems, and what support these methods offer to software developers. As such insight is important for researchers and engineers, we performed a systematic literature review covering 12 main software engineering venues and 4 journals, resulting in 75 papers used for data collection. The study shows that the attention for self-adaptive software systems is gradually increasing, but the number of studies that employ formal methods remains low. The main focus of formalization is on modeling and reasoning. Model checking and theorem proving have gained limited attention. The main concerns of interest in formalization of self-adaptation are efficiency/performance and reliability. Important adaptation concerns, such as security and scalability, are hardly considered. To verify the concerns of interest, a set of new properties are defined, such as interference freedom, responsiveness, mismatch, and loss-tolerance. A relevant part of the studies use formal methods at runtime, but the use is limited to modeling and analysis. Formal methods can be applied to other runtime activities of self-adaptation, and there is a need for light-weight tools to support runtime verification.

On interacting control loops in self-adaptive systems

Control loops in self-adaptive systems are typically conceived as a sequence of four computations: Monitor-Analyze-Plan-Execute (MAPE). During the development of a traffic monitoring system with support for self-healing, we have noticed that simple MAPE loops are not sufficient to deal with the more complex failure scenarios. To manage the adaptations in these scenarios, we extend MAPE loops with support for two types of coordination. First, we introduce support for intra-loop coordination enabling MAPE computations within one loop to coordinate with one another. Intra-loop coordination allows the execution of multiple sub-loops within one control loop. Second, we introduce support for inter-loop coordination enabling MAPE computations across multiple loops to coordinate with one another. Inter-loop coordination allows the MAPE computations of different loops to coordinate the various phases of adaptations. We show how we used the extensions to support self-healing in the traffic monitoring system. We discuss an implementation framework that supports coordination of MAPE loops, and from our experiences offer recommendations for future research in this area.