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@InProceedings{Pandey:SASO:2016,
AUTHOR = {Pandey, Ashutosh and Moreno, Gabriel A. and C\'{a}mara, Javier and Garlan, David},
TITLE = {Hybrid Planning for Decision Making in Self-Adaptive Systems},
YEAR = {2016},
MONTH = {12-16 September},
BOOKTITLE = {Proceedings of the 10th IEEE International Conference on Self-Adaptive and Self-Organizing Systems (SASO 2016)},
ADDRESS = {Augsburg, Germany},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/SASO2016.pdf},
ABSTRACT = {Run-time generation of adaptation plans is a powerful
mechanism that helps a self-adaptive system to meet its goals
in a dynamically changing environment. In the past, researchers
have demonstrated successful use of various automated planning
techniques to generate adaptation plans at run time. However, for
a planning technique, there is often a trade-off between timeliness
and optimality of the solution. For some self-adaptive systems,
ideally, one would like to have a planning approach that is both
quick and finds an optimal adaptation plan. To find the right
balance between these conflicting requirements, this paper introduces
a hybrid planning approach that combines more than one
planner to obtain the benefits of each. In this paper, to instantiate
a hybrid planner we combine deterministic planning with Markov
Decision Process (MDP) planning to obtain the best of both worlds:
deterministic planning provides plans quickly when timeliness is
critical, while allowing MDP planning to generate optimal plans
when the system has sufficient time to do so. We validate the
hybrid planning approach using a realistic workload pattern in
a simulated cloud-based self-adaptive system.},
KEYWORDS = {Planning, Self-adaptation}
}
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