mRUBiS: An Exemplar for Model-Based Architectural Self-Healing and Self-Optimization

Self-adaptive software systems are often structured into an adaptation engine that manages an adaptable software by operating on a runtime model that represents the architecture of the software (model-based architectural self-adaptation). Despite the popularity of such approaches, existing exemplars provide application programming interfaces but no runtime model to develop adaptation engines. Consequently, there does not exist any exemplar that supports developing, evaluating, and comparing model-based self-adaptation off the shelf. Therefore, we present mRUBiS, an extensible exemplar for model-based architectural self-healing and self-optimization. mRUBiS simulates the adaptable software and therefore provides and maintains an architectural runtime model of the software, which can be directly used by adaptation engines to realize and perform self-adaptation. Particularly, mRUBiS supports injecting issues into the model, which should be handled by self-adaptation, and validating the model to assess the self-adaptation. Finally, mRUBiS allows developers to explore variants of adaptation engines (e.g., event-driven self-adaptation) and to evaluate the effectiveness, efficiency, and scalability of the engines

MORPH: A Reference Architecture for Configuration and Behaviour Self-Adaptation

An architectural approach to self-adaptive systems involves runtime change of system configuration (i.e., the system's components, their bindings and operational parameters) and behaviour update (i.e., component orchestration). Thus, dynamic reconfiguration and discrete event control theory are at the heart of architectural adaptation. Although controlling configuration and behaviour at runtime has been discussed and applied to architectural adaptation, architectures for self-adaptive systems often compound these two aspects reducing the potential for adaptability. In this paper we propose a reference architecture that allows for coordinated yet transparent and independent adaptation of system configuration and behaviour

Towards a Framework for Managing Inconsistencies in Systems of Systems

The growth in the complexity of software systems has led to a proliferation of systems that have been created independently to provide specific functions, such as activity tracking, household energy management or personal nutrition assistance. The runtime composition of these individual systems into Systems of Systems (SoSs) enables support for more sophisticated functionality that cannot be provided by individual constituent systems on their own. However, in order to realize the benefits of these functionalities it is necessary to address a number of challenges associated with SoSs, including, but not limited to, operational and managerial independence, geographic distribution of participating systems, evolutionary development, and emergent conflicting behavior that can occur due interactions between the requirements of the participating systems. In this paper, we present a framework for conflict management in SoSs. The management of conflicting requirements involves four steps, namely (a) overlap detection, (b) conflict identification, (c) conflict diagnosis, and (d) conflict resolution based on the use of a utility function. The framework uses a Monitor-Analyze-Plan- Execute- Knowledge (MAPE-K) architectural pattern. In order to illustrate the work, we use an example SoS ecosystem designed to support food security at different levels of granularity

A Case Study on Formal Verification of Self-Adaptive Behaviors in a Decentralized System

Self-adaptation is a promising approach to manage the complexity of modern software systems. A self-adaptive system is able to adapt autonomously to internal dynamics and changing conditions in the environment to achieve particular quality goals. Our particular interest is in decentralized self-adaptive systems, in which central control of adaptation is not an option. One important challenge in self-adaptive systems, in particular those with decentralized control of adaptation, is to provide guarantees about the intended runtime qualities. In this paper, we present a case study in which we use model checking to verify behavioral properties of a decentralized self-adaptive system. Concretely, we contribute with a formalized architecture model of a decentralized traffic monitoring system and prove a number of self-adaptation properties for flexibility and robustness. To model the main processes in the system we use timed automata, and for the specification of the required properties we use timed computation tree logic. We use the Uppaal tool to specify the system and verify the flexibility and robustness properties.Comment: In Proceedings FOCLASA 2012, arXiv:1208.432

Identifying Conflicting Requirements in Systems of Systems

A System of Systems (SoS) is an arrangement of useful and independent sub-systems, which are integrated into a larger system. Examples are found in transport systems, nutritional systems, smart homes and smart cities. The composition of component sub-systems into an SoS enables support for complex functionalities that cannot be provided by individual sub-systems on their own. However, to realize the benefits of these functionalities it is necessary to address several software engineering challenges including, but not limited to, the specification, design, construction, deployment, and management of an SoS. The various component sub-systems in an SoS environment are often concerned with distinct domains; are developed by different stake-holders under different circumstances and time; provide distinct functionalities; and are used by different stakeholders, which allow for the existence of conflicting requirements. In this paper, we present a framework to support management of emerging conflicting requirements in an SoS. In particular, we describe an approach to support identification of conflicts between resource-based requirements (i.e. requirements concerned with the consumption of different resources). In order to illustrate and evaluate the work, we use an example of a pilot study of an IoT SoS ecosystem designed to support food security at different levels of granularity, namely individuals, groups, cities, and nations

Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution

Cloud controllers aim at responding to application demands by automatically scaling the compute resources at runtime to meet performance guarantees and minimize resource costs. Existing cloud controllers often resort to scaling strategies that are codified as a set of adaptation rules. However, for a cloud provider, applications running on top of the cloud infrastructure are more or less black-boxes, making it difficult at design time to define optimal or pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions often is delegated to the cloud application. Yet, in most cases, application developers in turn have limited knowledge of the cloud infrastructure. In this paper, we propose learning adaptation rules during runtime. To this end, we introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE learns and modifies fuzzy rules at runtime. The benefit is that for designing cloud controllers, we do not have to rely solely on precise design-time knowledge, which may be difficult to acquire. FQL4KE empowers users to specify cloud controllers by simply adjusting weights representing priorities in system goals instead of specifying complex adaptation rules. The applicability of FQL4KE has been experimentally assessed as part of the cloud application framework ElasticBench. The experimental results indicate that FQL4KE outperforms our previously developed fuzzy controller without learning mechanisms and the native Azure auto-scaling