Reference : Enabling Temporal-Aware Contexts for Adaptative Distributed Systems
Scientific journals : Article
Engineering, computing & technology : Computer science
http://hdl.handle.net/10993/35423
Enabling Temporal-Aware Contexts for Adaptative Distributed Systems
English
Mouline, Ludovic mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Benelallam, Amine mailto [Univ Rennes > Inria, CNRS, IRISA > DiverSE]
Hartmann, Thomas mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Fouquet, François mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
Bourcier, Johann mailto [Univ Rennes > Inria, CNRS, IRISA > DiverSE]
Morin, Brice mailto [SINTEF]
Barais, Olivier mailto [Univ Rennes > Inria, CNRS, IRISA > DiverSE]
9-Apr-2018
SAC 2018: SAC 2018: Symposium on Applied Computing , April 9--13, 2018, Pau, France
Yes
International
[en] model-driven engineering ; context modelling ; action modelling ; models@run.time
[en] Distributed adaptive systems are composed of federated entities offering remote inspection and reconfiguration abilities. This is often realized using a MAPE-K loop, which constantly evaluates system and environmental parameters and derives corrective actions if necessary. The OpenStack Watcher project uses such a loop to implement resource optimization services for multi-tenant clouds. To ensure a timely reaction in the event of failures, the MAPE-K loop is executed with a high frequency. A major drawback of such reactivity is that many actions, e.g., the migration of containers in the cloud, take more time to be effective and their effects to be measurable than the MAPE-k loop execution frequency. Unfinished actions as well as their expected effects over time are not taken into consideration in MAPE-K loop processes, leading upcoming analysis phases potentially take sub-optimal actions. In this paper, we propose an extended context representation for MAPE-K loop that integrates the history of planned actions as well as their expected effects over time into the context representations. This information can then be used during the upcoming analysis and planning phases to compare measured and expected context metrics. We demonstrate on a cloud elasticity manager case study that such temporal action-aware context leads to improved reasoners while still be highly scalable.
http://hdl.handle.net/10993/35423
10.1145/3167132.3167286

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