@Article{2018/Moreno/FlexibleLatency,
AUTHOR = {Moreno, Gabriel A. and C\'{a}mara, Javier and Garlan, David and Schmerl, Bradley},
TITLE = {Flexible and Efficient Decision-Making for Proactive
Latency-Aware Self-Adaptation},
YEAR = {2018},
MONTH = {May},
JOURNAL = {ACM Transactions on Autonomous and Adaptive Systems},
VOLUME = {13},
NUMBER = {1},
ABSTRACT = {Proactive latency-aware adaptation is an approach for self-adaptive systems that considers both the current
and anticipated adaptation needs when making adaptation decisions, taking into account the latency of the
available adaptation tactics. 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 has been used
to deal with this problem, but it requires constructing the MDP every time an adaptation decision is made
to incorporate the latest predictions of the environment behavior. In this article, we describe PLA-SDP, an
approach that eliminates that run-time overhead by constructing most of the MDP offine. At run time, the
adaptation decision is made by solving the MDP through stochastic dynamic programming, weaving in the
environment model as the solution is computed. We also present extensions that support different notions
of utility, such as maximizing reward gain subject to the satisfaction of a probabilistic constraint, making
PLA-SDP applicable to systems with different kinds of adaptation goals.},
NOTE = {https://doi.org/10.1145/3149180},
KEYWORDS = {Latency-aware, Self-adaptation}
}
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