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@Article{2018/MOSAICO,
AUTHOR = {C\'{a}mara, Javier and Schmerl, Bradley and Moreno, Gabriel A. and Garlan, David},
TITLE = {MOSAICO: Offline Synthesis of Adaptation Strategy Repertoires with Flexible Trade-Offs},
YEAR = {2018},
MONTH = {September},
JOURNAL = {Automated Software Engineering},
VOLUME = {25},
NUMBER = {3},
PAGES = { 595–626},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/Cámara2018_Article_MOSAICOOfflineSynthesisOfAdapt.pdf},
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.},
NOTE = {Springer Link},
KEYWORDS = {Formal Methods, Planning, Rainbow, Self-adaptation}
}
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