Stream my models: Reactive peer-to-peer distributed models@run.time

Hartmann, Thomas; Moawad, Assaad; Fouquet, Francois; Nain,; Klein, Jacques; Traon, Yves Le

doi:10.1109/models.2015.7338238

Cited by 19 publications

(13 citation statements)

References 44 publications

(40 reference statements)

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“…Hartmann et al [16] propose a solution to tackle scalability issues in the context of models@run.time by splitting models into chunks that are distributed across multiple nodes in a cluster. A lazy-loading mechanism allows to virtually access the entire model from each node.…”

Section: State Of the Artmentioning

confidence: 99%

Advanced prefetching and caching of models with PrefetchML

2018

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Caching and prefetching techniques have been used for decades in database engines and file systems to improve the performance of I/O intensive application. A prefetching algorithm typically benefits from the system's latencies by loading into main memory elements that will be needed in the future, speeding-up data access. While these solutions can bring a significant improvement in terms of execution time, prefetching rules are often defined at the data-level, making them hard to understand, maintain, and optimize. In addition, low-level prefetching and caching components are difficult to align with scalable model persistence frameworks because they are unaware of potential optimizations relying on the analysis of metamodel-level information, and are less present in NoSQL databases, a common solution to store large models. To overcome this situation we propose PrefetchML, a framework that executes prefetching and caching strategies over models. Our solution embeds a DSL to configure precisely the prefetching rules to follow, and a monitoring component providing insights on how the prefetching execution is working to help designers optimize his performance plans. Our experiments show that PrefetchML is a suitable solution to improve query execution time on top of scalable model persistence frameworks. Tool support is fully available online as an open-source Eclipse plugin.

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Section: State Of the Artmentioning

confidence: 99%

Advanced prefetching and caching of models with PrefetchML

2018

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“…Basically, every learning unit can be computed on a separate machine. Such distribution strategy relies on a shared model state, as for example presented in [22]. The computation can then be triggered in a bulk-synchronous parallel (BSP) way [15] over this shared state.…”

Section: Micro Learning Unitsmentioning

confidence: 99%

The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling

Hartmann

Moawad

Fouquet

et al. 2017

2017 ACM/IEEE 20th International Conference on Model Driven Engineering Languages and Systems (MODELS)

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Abstract-Machine learning algorithms are designed to resolve unknown behaviours by extracting commonalities over massive datasets. Unfortunately, learning such global behaviours can be inaccurate and slow for systems composed of heterogeneous elements, which behave very differently, for instance as it is the case for cyber-physical systems and Internet of Things applications. Instead, to make smart decisions, such systems have to continuously refine the behaviour on a per-element basis and compose these small learning units together. However, combining and composing learned behaviours from different elements is challenging and requires domain knowledge. Therefore, there is a need to structure and combine the learned behaviours and domain knowledge together in a flexible way. In this paper we propose to weave machine learning into domain modeling. More specifically, we suggest to decompose machine learning into reusable, chainable, and independently computable small learning units, which we refer to as micro learning units. These micro learning units are modeled together with and at the same level as the domain data. We show, based on a smart grid case study, that our approach can be significantly more accurate than learning a global behaviour while the performance is fast enough to be used for live learning.

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“…When an attribute, which is part of a rule definition, is modified, we navigate through the relations of a rule, process the condition graph and, if the condition is validated, get the action using its identifier and executed it. This process uses lazy loading techniques to dynamically load the necessary node on demand into main memory [22]. When a data model node is accessed, the system first looks into the main memory if the node is present.…”

Section: Rule Processingmentioning

confidence: 99%

Weaving Rules into Models@run.time for Embedded Smart Systems

Mouline

Hartmann

Fouquet

et al. 2017

Companion to the First International Conference on the Art, Science and Engineering of Programming

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Smart systems are characterised by their ability to analyse measured data in live and to react to changes according to expert rules. Therefore, such systems exploit appropriate data models together with actions, triggered by domain-related conditions. The challenge at hand is that smart systems usually need to process thousands of updates to detect which rules need to be triggered, often even on restricted hardware like a Raspberry Pi. Despite various approaches have been investigated to efficiently check conditions on data models, they either assume to fit into main memory or rely on high latency persistence storage systems that severely damage the reactivity of smart systems. To tackle this challenge, we propose a novel composition process, which weaves executable rules into a data model with lazy loading abilities. We quantitatively show, on a smart building case study, that our approach can handle, at low latency, big sets of rules on top of large-scale data models on restricted hardware.

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Stream my models: Reactive peer-to-peer distributed models@run.time

Cited by 19 publications

References 44 publications

Advanced prefetching and caching of models with PrefetchML

Advanced prefetching and caching of models with PrefetchML

The Next Evolution of MDE: A Seamless Integration of Machine Learning into Domain Modeling

Weaving Rules into Models@run.time for Embedded Smart Systems

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