@InProceedings{2016:Moreno:plasdp,
AUTHOR = {Moreno, Gabriel A. and C\'{a}mara, Javier and Garlan, David and Schmerl, Bradley},
TITLE = {Efficient Decision-Making under Uncertainty for Proactive Self-Adaptation},
YEAR = {2016},
MONTH = {19-22 July},
BOOKTITLE = {Proceedings of the 13th IEEE International Conference on Autonomic Computing (ICAC 2016)},
ADDRESS = {W\"{u}rzburg, Germany},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/gmoreno-icac2016.pdf},
ABSTRACT = {Proactive latency-aware adaptation is an approach
for self-adaptive systems that improves over reactive adaptation
by considering both the current and anticipated adaptation needs
of the system, and taking into account the latency of adaptation
tactics so that they can be started with the necessary lead time.
Making an adaptation decision with these characteristics requires
solving an optimization problem to select the adaptation path that
maximizes an objective function over a finite look-ahead horizon.
Since this is a problem of selecting adaptation actions in the
context of the probabilistic behavior of the environment, Markov
decision processes (MDP) are a suitable approach. However, given
all the possible interactions between the different and possibly
concurrent adaptation tactics, the system, and the environment,
constructing the MDP is a complex task. Probabilistic model
checking can be used to deal with this problem since it takes
as input a formal specification of the stochastic system, which
is internally translated into an MDP, and solved. One drawback
of this solution is that the MDP has to be constructed every
time an adaptation decision has to be made to incorporate the
latest predictions of the environment behavior. In this paper
we present an approach that eliminates that run-time overhead
by constructing most of the MDP offline, also using formal
specification. At run time, the adaptation decision is made by
solving the MDP through stochastic dynamic programming,
weaving in the stochastic environment model as the solution
is computed. Our experimental results show that this approach
reduces the adaptation decision time by an order of magnitude
compared to the probabilistic model checking approach, while
producing the same results.},
NOTE = {Selected as a best paper.},
KEYWORDS = {Latency-aware, Self-adaptation}
}
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