Weaving Rules into [email protected] for Embedded Smart Systems

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.Comment: pre-print version, published in the proceedings of MOMO-17 Worksho

A Temporal Model for Interactive Diagnosis of Adaptive Systems

The evolving complexity of adaptive systems impairs our ability to deliver anomaly-free solutions. Fixing these systems require a deep understanding on the reasons behind decisions which led to faulty or suboptimal system states. Developers thus need diagnosis support that trace system states to the previous circumstances –targeted requirements, input context– that had resulted in these decisions. However, the lack of efficient temporal representation limits the tracing ability of current approaches. To tackle this problem, we first propose a knowledge formalism to define the concept of a decision. Second, we describe a novel temporal data model to represent, store and query decisions as well as their relationship with the knowledge (context, requirements, and actions). We validate our approach through a use case based on the smart grid at Luxembourg. We also demonstrate its scalability both in terms of execution time and consumed memory

Enabling Temporal-Aware Contexts for Adaptative Distributed Systems

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

Raising Time Awareness in Model-Driven Engineering

International audienceThe conviction that big data analytics is a key for the success of modern businesses is growing deeper, and the mo-bilisation of companies into adopting it becomes increasingly important. Big data integration projects enable companies to capture their relevant data, to efficiently store it, turn it into domain knowledge, and finally monetize it. In this context, historical data, also called temporal data, is becoming increasingly available and delivers means to analyse the history of applications, discover temporal patterns, and predict future trends. Despite the fact that most data that today's applications are dealing with is inherently temporal current approaches, methodologies, and environments for developing these applications don't provide sufficient support for handling time. We envision that Model-Driven Engineering (MDE) would be an appropriate ecosystem for a seamless and orthogonal integration of time into domain modelling and processing. In this paper, we investigate the state-of-the-art in MDE techniques and tools in order to identify the missing bricks for raising time-awareness in MDE and outline research directions in this emerging domain

KevoreeJS: Enabling dynamic software reconfigurations in the Browser

International audienceThe architecture of classic productivity software are moving from a traditional desktop-based software to a client server architecture hosted in the Cloud. In this context, web browsers behave as application containers that allow users to access a variety of Cloud-based applications and services, such as IDEs, Word processors, Music Collection Managers, etc. As a result, a significant part of these software run in the browser and accesses remote services. A lesson learned from development framework used in distributed applications is the success of pluggable architecture pattern as a core architecture concept, i.e., a Software Architecture that promotes the use of Pluggable Module to dynamically plug. Following this trend, this paper discusses the main challenges to create a component-based platform supporting the development of dynamically adaptable single web page applications. This paper also presents an approach called KevoreeJS based on models@runtime to control browser as component platform which address some of these challenges. We validate this work by presenting the design of a dashboard for sensor based system and highlighting the capacity of KevoreeJS to dynamically choose the placement of code on the server or client side and how KevoreeJS can be used to dynamically install or remove running components

Vers un framework de modélisation avec des données temporelles et incertaines pour les systèmes adaptatifs

Self-Adaptive Systems (SAS) optimise their behaviours or configurations at runtime in response to a modification of their environments or their behaviours. These systems therefore need a deep understanding of the ongoing situation which enables reasoning tasks for adaptation operations. Using the model-driven engineering (MDE) methodology, one can abstract this situation. However, information concerning the system is not always known with absolute confidence. Moreover, in such systems, the monitoring frequency may differ from the delay for reconfiguration actions to have measurable effects. These characteristics come with a global challenge for software engineers: how to represent uncertain knowledge that can be efficiently queried and to represent ongoing actions in order to improve adaptation processes? To tackle this challenge, this thesis defends the need for a unified modelling framework which includes, besides all traditional elements, temporal and uncertainty as first-class concepts. Therefore, a developer will be able to abstract information related to the adaptation process, the environment as well as the system itself. Towards this vision, we present two evaluated contributions: a temporal context model and a language for uncertain data. The temporal context model allows abstracting past, ongoing and future actions with their impacts and context. The language, named Ain’tea, integrates data uncertainty as a first-class citizen.Les systèmes auto-adaptatifs (SAS) optimisent leurs comportements ou configurations au moment de l'exécution en réponse à une modification de leur environnement ou de leurs comportements. Ces systèmes nécessitent donc une connaissance approfondie de la situation en cours qui permet de raisonnement en considérant les opérations d'adaptation. En utilisant la méthodologie de l'Ingénierie Dirigée par les Modèles (IDM), il est possible d'abstraire cette situation. Cependant, les informations concernant le système ne sont pas toujours connues avec une confiance absolue. De plus, dans de tels systèmes, la fréquence de surveillance peut différer du délai nécessaire pour que les mesures de reconfiguration aient des effets mesurables. Ces caractéristiques s'accompagnent d'un défi global pour les ingénieurs logiciels : comment représenter les connaissances incertaines tout en permettant de les interroger efficacement et de représenter les actions en cours afin d'améliorer les processus d'adaptation ? Pour relever ce défi, cette thèse défend la nécessité d'un framework de modélisation qui inclut, en plus de tous les éléments traditionnels, l'incertitude et le temps en tant que concepts de première classe. Par conséquent, un développeur sera en mesure d'extraire des informations relatives au processus d'adaptation, à l'environnement ainsi qu'au système lui-même. Dans cette optique, nous présentons deux contributions évaluées : un modèle de contexte temporel et un langage pour les données incertaines. Le modèle de contexte temporel permet d'abstraire les actions passées, en cours et futures avec leurs impacts et leur contexte. Le langage, appelé Ain'tea, intègre l'incertitude des données en tant que concept de première classe

TOWARDS A MODELLING FRAMEWORK WITH TEMPORAL AND UNCERTAIN DATA FOR ADAPTIVE SYSTEMS

Self-Adaptive Systems (SAS) optimise their behaviours or configurations at runtime in response to a modification of their environments or their behaviours. These systems therefore need a deep understanding of the ongoing situation which enables reasoning tasks for adaptation operations. Using the model-driven engineering (MDE) methodology, one can abstract this situation. However, information concerning the system is not always known with absolute confidence. Moreover, in such systems, the monitoring frequency may differ from the delay for reconfiguration actions to have measurable effects. These characteristics come with a global challenge for software engineers: how to represent uncertain knowledge that can be efficiently queried and to represent ongoing actions in order to improve adaptation processes? To tackle this challenge, this thesis defends the need for a unified modelling framework which includes, besides all traditional elements, temporal and uncertainty as first-class concepts. Therefore, a developer will be able to abstract information related to the adaptation process, the environment as well as the system itself. Towards this vision, we present two evaluated contributions: a temporal context model and a language for uncertain data. The temporal context model allows abstracting past, ongoing and future actions with their impacts and context. The language, named Ain’tea, integrates data uncertainty as a first-class citizen

Load approximation for uncertain topologies in the low-voltage grid

Smart grids allow operators to monitor the grid continuously, detect occurring incidents, and trigger corrective actions. To perform that, they require a deep understanding of the effective situation within the grid. However, some parameters of the grid may not be known with absolute confidence. Reasoning over the grid despite uncertainty needs the consideration of all possible states. In this paper, we propose an approach to enumerate only valid potential grid states. Thereby, we allow discarding invalid assumptions that poison the results of a given computation procedure. We validate our approach based on a real-world topology from the power grid in Luxembourg. We show that the estimation of cable load is negatively affected by invalid fuse state combinations, in terms of computation time and accuracy

Enabling Temporal-Aware Contexts for Adaptative Distributed Systems Temporal Context Representation System updates

International audienceDistributed 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. CCS CONCEPTS • Software and its engineering → Software design engineering ; • Computer systems organization → Reconfigurable computing ; Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only. Adaptive process used as knowledge for re-configures adds actions with their timed effects reads next actions Scheduler Figure 1: Overview of temporal-aware adaptatio