Comparing Model-Based Predictive Approaches to Self-Adaptation: CobRA and PLA
Gabriel A. Moreno, Alessandro V. Papadopoulos, Konstantinos Angelopoulo,
Javier Cámara and
Bradley Schmerl.
In Proceedings of the 12th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS 2017), Buenos Aires, Argentina, 22-23 May 2017.
Online links:   Plain Text
Abstract
| Modern software-intensive systems must often guarantee
certain quality requirements under changing run-time
conditions and high levels of uncertainty. Self-adaptation has
proven to be an effective way to engineer systems that can
address such challenges, but many of these approaches are purely
reactive and adapt only after a failure has taken place. To
overcome some of the limitations of reactive approaches (e.g.,
lagging behind environment changes and favoring short-term
improvements), recent proactive self-adaptation mechanisms apply
ideas from control theory, such as model predictive control
(MPC), to improve adaptation. When selecting which MPC
approach to apply, the improvement that can be obtained with
each approach is scenario-dependent, and so guidance is needed
to better understand how to choose an approach for a given
situation. In this paper, we compare CobRA and PLA, two
approaches that are inspired by MPC. CobRA is a requirements-based
approach that applies control theory, whereas PLA is
architecture-based and applies stochastic analysis. We compare
the two approaches applied to RUBiS, a benchmark system for
web and cloud application performance, discussing the required
expertise needed to use both approaches and comparing their
run-time performance with respect to different metrics. |
Keywords: Self-adaptation.
|
|
