Felix Maximilian Roth

A survey on engineering approaches for self-adaptive systems

The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation.

Towards Reusability in Autonomic Computing

Reusability of software artifacts reduces development time, effort, and error-proneness. Nevertheless, in the development of autonomic systems, developers often start from scratch when building a new system instead of reusing existing components. Many frameworks offer reusability on a higher level of abstraction, but neglect reusability on the lower component implementation level. In this short paper, we present a reusable adaptation logic by separating the generic structure and mechanisms of Autonomic Computing systems from its custom functionality. That is, we provide a reusable communication architecture with abstract component templates that enables a faster development and easier runtime adaptation. We evaluate our approach in a case study with two implementations.

Runtime Evolution of the Adaptation Logic in Self-Adaptive Systems

Self-adaptive systems, which are highly related to Autonomic Computing, are a response to the increasing complexity and size of information systems. They are able to adapt their behavior to changes in the environment or system resources. A self-adaptive system consists of managed resources that realize functionality and an adaptation logic that controls the adaptations. So far, many research has been performed on adapting the managed resources. However, only few works cover adapting the adaptation logic, which might be necessary in several cases, e.g., When the architecture of the managed resources changes. This work adresses why adaptation of the adaptation logic might be beneficial, how it can be achieved, and what challenges arise.

FESAS IDE: An Integrated Development Environment for Autonomic Computing

While Autonomic Computing can ease the maintenance of systems through adaptations [1], the development of Autonomic Computing systems itself introduces a high complexity. Literature suggests that reusable processes for the development and reusable components in the adaptation logic can reduce the complexity. Existing approaches aim to reduce this complexity with tools and frameworks for specific tasks in the development of the adaptation logic of Autonomic Computing systems. However, to the best of our knowledge, none of these approaches offer an Integrated Development Environment (IDE) for it. In this paper, we extend FESAS - our framework for building reusable adaptation logic components - with Eclipse plug-ins integrated into the FESAS IDE for a simplified development of MAPE components as well as a process for deployment of the components. In this paper, we present these tools. Further, we evaluate their potential to ease the development of self-adaptive systems within five example cases. Last, we discuss the benefits and limitations of the FESAS IDE.

Comparison of Approaches for Self-Improvement in Self-Adaptive Systems

Various trends such as mobility of devices, Cloud Computing, or Cyber-Physical Systems lead to a higher degree of distribution. These systems-of-systems need to be integrated. The integration of various subsystems still remains a challenge. Self-improvement within self-adaptive systems can help to shift integration tasks from the static design time to the runtime, which fits the dynamic needs of these systems. Thus, it can enable the integration of system parts at runtime. In this paper, we define self-improvement as an adaptation of an Autonomic Computing systems adaptation logic. We present an overview of approaches for self-improvement in the domains of Autonomic Computing and self-adaptive systems. Based on a taxonomy for self-adaptation, we compare the approaches and categorize them. The categorization shows that the approaches focus either on structural or parameter adaptation but seldomly combine both. Based on the categorization, we elaborate challenges, that need to be addressed by future approaches for offering self-improving system integration at runtime.

Nature-Inspired Interference Management in Smart Peer Groups

Applications in intelligent environments are context-aware and context-altering in order to support users in their everyday tasks. In multi-user environments with shared context, interferences are likely to occur. An interference is an application-induced context that forces other applications to react. In our COMITY project, we developed interference detection and resolution algorithms. However, especially the constraint satisfaction-based resolution algorithm is computationally expensive. It requires a full-fledged machine in order to achieve runtimes suitable for interactive systems. In spontaneously formed smart peer groups, such infrastructure is not given. In this paper, we present an approach for detecting and resolving interferences in smart peer groups. The approach is inspired by two coordination mechanisms from nature. We map the problems of interference detection and resolution to the rules of the flocking mechanism, show how to implement a flocking-based interference management, and present a local inhibition-based leader election for smart peer groups in order to elect the coordinating entity. Finally, we show the feasibility of our approach by evaluating our prototype.

XWARE - a customizable interoperability framework for pervasive computing systems

Abstract Middleware support for Pervasive Computing has been extensively researched in the past. Different approaches for, e.g., discovery, communication, or interaction, have been explored and successfully tested in different lab environments. Although this works well in lab environments, where one middleware manages all resources, this will not suffice in real-world deployments, where more than one system software is present and services as well as other resources are only accessible by those. In this paper, we present XWARE, an interoperability framework that allows to integrate discovery and interaction of different middleware platforms. The flexible design allows to configure and extend the interoperability framework in order to add new platforms and tailor it for the use in different domains. Furthermore, XWARE instances can communicate with each other enabling interoperability in smart environments as well as in smart peer groups. The feasibility of our approach is discussed and evaluated based on the integration of five different platforms.

Adding Self-Improvement to an Autonomic Traffic Management System

Autonomic Computing and self-adaptive systems are a response to the increasing complexity required to cope with changing environments and varying system resources. However, the complexity of the adaptation logic itself increases with the available information in particular for distributed systems. This leads to uncertainty at runtime resulting in incompleteness in the representation of adaptation goals, models, or rules. Self-improvement which changes the adaptation logic at runtime through meta-adaptation addresses the uncertainty issue.In this paper, we present and discuss a self-improvement case study for an autonomic traffic management system. We adapt parameters of the adaptation logic through rule learning as well as the structure of the adaptation logic, e.g., from central to decentralized control. We show that the resulting implementation enables continuous self-improvement of the system even in situations that have not been taken into account at design time.