Reducing Run-Time Adaptation Space via Analysis of Possible Utility Bounds

Self-adaptive systems often employ dynamic programming or similar techniques to select optimal adaptations at run-time. These techniques suffer from the “curse of dimensionality , increasing the cost of run-time adaptation decisions. We propose a novel approach that improves upon the state-of-the-art proactive self-adaptation techniques to reduce the number of possible adaptations that need be considered for each run-time adaptation decision. The approach, realized in a tool called Thallium, employs a combination of automated formal modeling techniques to (i) analyze a structural model of the system showing which configurations are reachable from other configurations and (ii) compute the utility that can be generated by the optimal adaptation over a bounded horizon in both the best- and worst-case scenarios. It then constructs triangular possibility values using those optimized bounds to automatically compare adjacent adaptations for each configuration, keeping only the alternatives with the best range of potential results. The experimental results corroborate Thallium’s ability to significantly reduce the number of states that need to be considered with each adaptation decision, freeing up vital resources at run-time

What Can Control Theory Teach Us About Assurances in Self-Adaptive Software Systems?

International audienceSelf-adaptive software (SAS) systems monitor their own behavior and autonomously make dynamic adjustments to maintain desired properties in response to changes in the systems’ operational contexts. Control theory provides verifiable feedback models to realize this kind of autonomous control for a broad class of systems for which precise quantitative or logical discrete models can be defined. Recent MAPE-K models, and variants such as the hierarchical ACRA and similar others, address a broader range of tasks, but they do not provide the inherent assurances that control theory does, as they do not explicitly identify the properties that reliable controllers should have. These properties, in general, result not from the abstract models, but from the specifics of control strategies, which precisely these models fail to analyze. We show that, even for systems too complex for direct application of classical control theory, the abstractions of control theory provide design guidance that identifies important control characteristics and raises critical design issues about the details of the strategy that determine the controllability of the resulting systems. This in turn enables careful reasoning about whether the control characteristics are in fact achieved. In this chapter we examine the control theory approach, explain several control strategies illustrated with examples from both domains, classical control theory and SAS, and show how the issues addressed by these strategies can and should be seriously considered in the design of self-adaptive software systems. From this examination we distill challenges for developing principles that may serve as the basis of a control theory for self-adaptive software systems

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 adaptive solutions, processes, from centralized to decentralized control, and practical run-time verification and validation. 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

Software engineering for self-adaptive systems:research challenges in the provision of assurances

The important concern for modern software systems is to become more cost-effective, while being versatile, flexible, resilient, dependable, energy-efficient, customisable, configurable and self-optimising when reacting to run-time changes that may occur within the system itself, its environment or requirements. One of the most promising approaches to achieving such properties is to equip software systems with self-managing capabilities using self-adaptation mechanisms. Despite recent advances in this area, one key aspect of self-adaptive systems that remains to be tackled in depth is the provision of assurances, i.e., the collection, analysis and synthesis of evidence that the system satisfies its stated functional and non-functional requirements during its operation in the presence of self-adaptation. The provision of assurances for self-adaptive systems is challenging since run-time changes introduce a high degree of uncertainty. This paper on research challenges complements previous roadmap papers on software engineering for self-adaptive systems covering a different set of topics, which are related to assurances, namely, perpetual assurances, composition and decomposition of assurances, and assurances obtained from control theory. This research challenges paper is one of the many results of the Dagstuhl Seminar 13511 on Software Engineering for Self-Adaptive Systems: Assurances which took place in December 2013