Lazy Repairing Backtracking for Dynamic Constraint Satisfaction Problems

Extended Partial Dynamic Backtracking (EPDB) is a repair algorithm based on PDB. It deals with Dynamic CSPs based on ordering heuristics and retroactive data structures, safety conditions, and nogoods which are saved during the search process. In this paper, we show that the drawback of both EPDB and PDB is the exhaustive verification of orders, saved in safety conditions and nogoods, between variables. This verification affects remarkably search time, especially since orders are often indirectly deduced. Therefore, we propose a new approach for dynamically changing environments, the Lazy Repairing Backtracking (LRB), which is a fast version of EPDB insofar as it deduces orders directly through the used ordering heuristic. We evaluate LRB on various kinds of problems, and compare it, on the one hand, with EPDB to show its effectiveness compared to this approach, and, on the other hand, with MAC-2001 in order to conclude, from what perturbation rate resolving a DCSP with an efficient approach can be more advantageous than repair

Improving memory efficiency for processing large-scale models

International audienceScalability is a main obstacle for applying Model-Driven Engineering to reverse engineering, or to any other activity manipulating large models. Existing solutions to persist and query large models are currently ine cient and strongly linked to memory availability. In this paper, we propose a memory unload strategy for Neo4EMF, a persistence layer built on top of the Eclipse Modeling Framework and based on a Neo4j database backend. Our solution allows us to partially unload a model during the execution of a query by using a periodical dirty saving mechanism and transparent reloading. Our experiments show that this approach enables to query large models in a restricted amount of memory with an acceptable performance

ATL-MR: Model Transformation on MapReduce

International audienceThe Model-Driven Engineering (MDE) paradigm has been successfully embraced for manufacturing maintainable software in several domains while decreasing costs and efforts. One of its principal concepts is rule-based Model Transformation (MT) that enables an automated processing of models for different intentions. The user-friendly syntax of MT languages is designed for allowing users to specify and execute these operations in an effortless manner. Existing MT engines, however, are incapable of accomplishing transformation operations in an acceptable time while facing complex transformations. Worse, against large amount of data, these tools crash throwing an out of memory exception. In this paper, we introduce ATL-MR, a tool to automatically distribute the execution of model transformations written in a popular MT language, ATL, on top of a well-known distributed programming model, MapReduce. We briefly present an overview of our approach, we describe the changes with respect to the standard ATL transformation engine, finally , we experimentally show the scalability of this solution

Decentralized Model Persistence for Distributed Computing

International audienceThe necessity of manipulating very large amounts of data and the wide availability of computational resources on the Cloud is boosting the popularity of distributed computing in industry. The applicability of model-driven engineering in such scenarios is hampered today by the lack of an efficient model-persistence framework for distributed computing. In this paper we present NeoEMF/HBase, a persistence backend for the Eclipse Modeling Framework (EMF) built on top of the Apache HBase data store. Model distribution is hidden from client applications , that are transparently provided with the model elements they navigate. Access to remote model elements is decentralized, avoiding the bottleneck of a single access point. The persistence model is based on key-value stores that allow for efficient on-demand model persistence

Towards an Open Set of Real-World Benchmarks for Model Queries and Transformations

International audienceWith the growing size and complexity of systems under design, industry needs a generation of Model-Driven Engineering (MDE) tools, especially model query and transformation, with the proven capability to handle large-scale scenarios. While researchers are proposing several technical solutions in this sense, the community lacks a set of shared scalability benchmarks, that would simplify quantitative assessment of advancements and enable cross-evaluation of different proposals. Benchmarks in previous work have been synthesized to stress specific features of model management, lacking both generality and industrial validity. In this paper, we initiate an effort to define a set of shared benchmarks, gathering queries and transformations from real-world MDE case studies. We make these case available to community evaluation via a public MDE benchmark repository

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

Efficient Model Partitioning for Distributed Model Transformations

International audienceAs the models that need to be handled in model-driven engineering grow in scale, scalable algorithms for model transformation (MT) are becoming necessary. Programming models such as MapReduce or Pregel may simplify the development of distributed model transformations. However, because of the dense inter-connectivity of models and the complexity of transformation logics, scalability in distributed model processing is challenging. In this paper, we adapt existing formalization of uniform graph partitioning to the case of distributed MTs by means of binary linear programming. Moreover, we propose a data distribution algorithm for declarative model transformation based on static analysis of relational transformation rules. We first extract footprints from transformation rules. Then we propose a fast data distribution algorithm, driven by the extracted footprints, and based on recent results on balanced partitioning of streaming graphs. To validate our approach, we apply it to an existing distributed MT engine for the ATL language, built on top of MapReduce. We implement our heuristic as a custom split algorithm for ATL on MapReduce and we evaluate its impact on remote access to the underlying backend

Analyzing 2.3 Million Maven Dependencies to Reveal an Essential Core in APIs

This paper addresses the following question: does a small, essential, core set of API members emerges from the actual usage of the API by client applications? To investigate this question, we study the 99 most popular libraries available in Maven Central and the 865,560 client programs that declare dependencies towards them, summing up to 2.3M dependencies. Our key findings are as follows: 43.5% of the dependencies declared by the clients are not used in the bytecode; all APIs contain a large part of rarely used types and a few frequently used types, and the ratio varies according to the nature of the API, its size and its design; we can systematically extract a reuse-core from APIs that is sufficient to provide for most clients, the median size of this subset is 17% of the API that can serve 83% of the clients. This study is novel both in its scale and its findings about unused dependencies and the reuse-core of APIs. Our results provide concrete insights to improve Maven's build process with a mechanism to detect unused dependencies. They also support the need to reduce the size of APIs to facilitate API learning and maintenance.Comment: 15 pages, 13 figures, 3 tables, 2 listing

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

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