SHOW publication #638 - ABLE Publications

Plan reuse is a promising approach for enabling self-* systems to effectively adapt to unexpected changes, such as evolving existing adaptation strategies after an unexpected change using stochastic search. An ideal self-* planner should be able to reuse repertoires of adaptation strategies, but this is challenging due to the evaluation overhead. For effective reuse, a repertoire should be both (a) likely to generalize to future situations, and (b) cost effective to evaluate. In this work, we present an approach inspired by chaos engineering for generating a diverse set of adaptation strategies to reuse, and we explore two analysis approaches based on clone detection and syntactic transformation for constructing repertoires of adaptation tactics that are likely to be amenable to reuse in stochastic search self-* planners. An evaluation of the proposed approaches on a simulated system inspired by Amazon AWS shows planning effectiveness improved by up to 20% and reveals tradeoffs in planning timeliness and optimality.

@InProceedings{2020:ACSOS:SelfStar,
AUTHOR = {Kinneer, Cody and Van Tonder, Rijnard and Garlan, David and Le Goues, Claire},
TITLE = {Building Reusable Repertoires for Stochastic Self-* Planners},
YEAR = {2020},
MONTH = {17-21 August},
BOOKTITLE = {Proceedings of the 2020 IEEE Conference on Autonomic Computing and Self-organizing Systems (ACSOS)},
ADDRESS = {Washington, D.C., USA},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/ACSOS_2020_Building_Reusable_Repertoires.pdf},
ABSTRACT = {Plan reuse is a promising approach for enabling self-* systems to effectively adapt to unexpected changes, such as evolving existing adaptation strategies after an unexpected change using stochastic search. An ideal self-* planner should be able to reuse repertoires of adaptation strategies, but this is challenging due to the evaluation overhead. For effective reuse, a repertoire should be both (a) likely to generalize to future situations, and (b) cost effective to evaluate. In this work, we present an approach inspired by chaos engineering for generating a diverse set of adaptation strategies to reuse, and we explore two analysis approaches based on clone detection and syntactic transformation for constructing repertoires of adaptation tactics that are likely to be amenable to reuse in stochastic search self-* planners. An evaluation of the proposed approaches on a simulated system inspired by Amazon AWS shows planning effectiveness improved by up to 20% and reveals tradeoffs in planning timeliness and optimality.},
NOTE = {Presentation Video},
KEYWORDS = {Self-adaptation, Self-Repair, Stochastic Search}
}