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MOSAICO: offline synthesis of adaptation strategy repertoires with flexible trade-offs

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Abstract

Self-adaptation improves the resilience of software-intensive systems, enabling them to adapt their structure and behavior to run-time changes (e.g., in workload and resource availability). Many of these approaches reason about the best way of adapting by synthesizing adaptation plans online via planning or model checking tools. This method enables the exploration of a rich solution space, but optimal solutions and other guarantees (e.g., constraint satisfaction) are computationally costly, resulting in long planning times during which changes may invalidate plans. An alternative to online planning involves selecting at run time the adaptation best suited to the current system and environment conditions from among a predefined repertoire of adaptation strategies that capture repair and optimization tasks. This method does not incur run-time overhead but requires additional effort from engineers, who have to specify strategies and lack support to systematically assess their quality. In this article, we present MOSAICO, an approach for offline synthesis of adaptation strategy repertoires that makes a novel use of discrete abstractions of the state space to flexibly adapt extra-functional behavior in a scalable manner. The approach supports making trade-offs: (i) among multiple extra-functional concerns, and (ii) between computation time and adaptation quality (varying abstraction resolution). Our results show a remarkable improvement on system qualities in contrast to manually-specified repertoires. More interestingly, moderate increments in abstraction resolution can lead to pronounced quality improvements, whereas high resolutions yield only negligible improvement over medium resolutions.

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Notes

  1. Other instantiations of scenarios based on Znn consider fidelity/service level as a primary objective. However, we assume that fidelity is dominated by performance and user annoyance in overall user experience. Hence, fidelity adjustment is considered only as a mechanism to affect performance in our scenario. A more detailed rationale for the extra-functional requirements of this scenario can be found in Schmerl et al. (2014).

  2. This specification is semi-automated. Specifically, the most repetitive and error-prone parts of the specification of the formal model, like the encoding of the utility profile and tactic impact models are automated.

  3. The sources and process required to obtain the information needed to generate the artifacts required by a Stitch adaptation model are discussed in Cheng (2008).

  4. We consider fixed impacts for illustration purposes, although Stitch also supports the specification probabilistic/context-sensitive impact models (Cámara et al. 2014). Note that, to obtain the impact on the different quality dimensions of tactics in practice, the approach relies on expert knowledge, although nothing prevents the use of machine learning techniques to obtain that information [c.f. Didona and Romano (2015)].

  5. By default, probabilities are divided equally among the branches, although they can be progressively adjusted according to information collected from system executions. Alternatively, probabilities can also be bootstrapped based on knowledge obtained from experts or existing similar systems, when available.

  6. Policies are also commonly referred to as strategies or adversaries. In this article, we employ the term policy consistently to avoid confusion with the term adaptation strategy.

  7. We illustrate our approach to modeling the parametric model using the syntax of the PRISM language (Kwiatkowska et al. 2011) for Markov Decision Processes (MDPs), which are encoded as commands:

    $$\begin{aligned} {[}action] guard -> p_1:u_1+ \dots + p_n:u_n \end{aligned}$$

    Where guard is a predicate over the model variables. Each update \(u_i\) describes a transition that the process can make (by executing action) if the guard is true. An update is specified by giving the new values of the variables, and has an assigned probability \(p_i \in [0,1]\). Multiple commands with overlapping guards (and probably, including a single update of unspecified probability) introduce local nondeterminism.

  8. Thresholds are defined based on expert knowledge, and are analogous to the ones found in existing Stitch models for Znn Cheng and Garlan (2012).

  9. Function \(extract\_strategy\) abstracts the extraction of a sequence of actions from a policy, which is trivial (see Definition 2).

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Acknowledgements

This material is based upon work funded and supported by the Department of Defense under Contract No. FA8721-05-C-0003 with Carnegie Mellon University for the operation of the Software Engineering Institute, a federally funded research and development center. [Distribution Statement A] This material has been approved for public release and unlimited distribution. Please see Copyright notice for non-US Government use and distribution (DM-0004612).

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Authors and Affiliations

  1. School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Javier Cámara, Bradley Schmerl & David Garlan

  2. Software Engineering Institute, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Gabriel A. Moreno

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  1. Javier Cámara
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  2. Bradley Schmerl
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  3. Gabriel A. Moreno
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Correspondence to Javier Cámara.

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Cámara, J., Schmerl, B., Moreno, G.A. et al. MOSAICO: offline synthesis of adaptation strategy repertoires with flexible trade-offs. Autom Softw Eng 25, 595–626 (2018). https://doi.org/10.1007/s10515-018-0234-9

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