Proactive Self-Adaptation under Uncertainty: a Probabilistic Model Checking Approach
Gabriel A. Moreno,
Javier Cámara,
David Garlan and
Bradley Schmerl.
In Proceedings of the 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, Bergamo, Italy, 30 August - 4 September 2015.
Online links: 
Abstract
| Self-adaptive systems tend to be reactive and myopic, adapting
in response to changes without anticipating what the
subsequent adaptation needs will be. Adapting reactively
can result in inefficiencies due to the system performing a
suboptimal sequence of adaptations. Furthermore, when
adaptations have latency, and take some time to produce
their e ffect, they have to be started with sufficient lead time
so that they complete by the time their eff ect is needed.
Proactive latency-aware adaptation addresses these issues
by making adaptation decisions with a look-ahead horizon
and taking adaptation latency into account. In this paper we
present an approach for proactive latency-aware adaptation
under uncertainty that uses probabilistic model checking for
adaptation decisions. The key idea is to use a formal model
of the adaptive system in which the adaptation decision is
left underspeci fied through nondeterminism, and have the
model checker resolve the nondeterministic choices so that
the accumulated utility over the horizon is maximized. The
adaptation decision is optimal over the horizon, and takes
into account the inherent uncertainty of the environment
predictions needed for looking ahead. Our results show that
the decision based on a look-ahead horizon, and the factoring
of both tactic latency and environment uncertainty, considerably
improve the eff ectiveness of adaptation decisions. |
Keywords: Latency-aware, Model Checking, Self-adaptation, Self-awareness & Adaptation, Stochastic Games.
@InProceedings{2015/Moreno/PMC,
AUTHOR = {Moreno, Gabriel A. and C\'{a}mara, Javier and Garlan, David and Schmerl, Bradley},
TITLE = {Proactive Self-Adaptation under Uncertainty: a Probabilistic Model Checking Approach},
YEAR = {2015},
MONTH = {30 August - 4 September},
BOOKTITLE = {Proceedings of the 10th Joint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering},
ADDRESS = {Bergamo, Italy},
PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/fse15main-mainid213-p-1d01012-24621-final.pdf},
ABSTRACT = {Self-adaptive systems tend to be reactive and myopic, adapting
in response to changes without anticipating what the
subsequent adaptation needs will be. Adapting reactively
can result in inefficiencies due to the system performing a
suboptimal sequence of adaptations. Furthermore, when
adaptations have latency, and take some time to produce
their e ffect, they have to be started with sufficient lead time
so that they complete by the time their eff ect is needed.
Proactive latency-aware adaptation addresses these issues
by making adaptation decisions with a look-ahead horizon
and taking adaptation latency into account. In this paper we
present an approach for proactive latency-aware adaptation
under uncertainty that uses probabilistic model checking for
adaptation decisions. The key idea is to use a formal model
of the adaptive system in which the adaptation decision is
left underspeci fied through nondeterminism, and have the
model checker resolve the nondeterministic choices so that
the accumulated utility over the horizon is maximized. The
adaptation decision is optimal over the horizon, and takes
into account the inherent uncertainty of the environment
predictions needed for looking ahead. Our results show that
the decision based on a look-ahead horizon, and the factoring
of both tactic latency and environment uncertainty, considerably
improve the eff ectiveness of adaptation decisions.},
KEYWORDS = {Latency-aware, Model Checking, Self-adaptation, Self-awareness & Adaptation, Stochastic Games}
}
|
