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ACM/IEEE International Conference on model driven Engineering Languages and Systems (modelS)

作者： Gemeinhardt, Felix Eisenberg, Martin Klikovits, Stefan Wimmer, Manuel Johannes Kepler Univ Linz Inst Business Informat Software Engn Linz Austria

ISBN: (纸本)9798350324983

In the realm of classical software engineering, model-driven optimization has been widely used for different problems such as (re)modularization of software systems. In this paper, we investigate how techniques from model-driven optimization can be applied in the context of quantum software engineering. In quantum computing, creating executable quantum programs is a highly non-trivial task which requires significant expert knowledge in quantum information theory and linear algebra. Although different approaches for automated quantum program synthesis exist-e.g., based on reinforcement learning and genetic programming-these approaches represent tailor-made solutions requiring dedicated encodings for quantum programs. This paper applies the existing model-driven optimization approach MOMoT to the problem of quantum program synthesis. We present the resulting platform for experimenting with quantum program synthesis and present a concrete demonstration for a well-known quantum algorithm.

关键词： Quantum Circuit Synthesis model-driven optimization Quantum Software Engineering

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Towards a Configurable Crossover Operator for model-driven optimization 22

Towards a Configurable Crossover Operator for Model-Driven O...

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ACM/IEEE 25th International Conference on model driven Engineering Languages and Systems (modelS)

作者： John, Stefan Kosiol, Jens Taentzer, Gabriele Philipps Univ Marburg Marburg Germany

ISBN: (纸本)9781450394673

In evolutionary algorithms, mutation and crossover are used to explore a search space for solutions. For the model-based approach to model-driven optimization, where models are used to represent solutions, no crossover operator has been introduced yet. However, theoretical and experimental evidence shows that evolutionary search can benefit from the use of crossover. We present a configurable crossover operator for models defined in the Eclipse modeling Framework (EMF), discuss several variants of incorporating domain knowledge into this operator, and argue that it produces EMF models again. We also present a prototype implementation of our crossover operator and conduct an initial evaluation to investigate the effectiveness of evolutionary computations that use both mutation and crossover.

关键词： search-based software engineering model-driven optimization evolutionary computation crossover

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model-driven optimization of Opportunistic Routing

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IEEE-ACM TRANSACTIONS ON NETWORKING 2013年第2期21卷 594-609页

作者： Rozner, Eric Han, Mi Kyung Qiu, Lili Zhang, Yin Univ Texas Austin Austin TX 78712 USA

Opportunistic routing aims to improve wireless performance by exploiting communication opportunities arising by chance. A key challenge in opportunistic routing is how to achieve good, predictable performance despite the incidental nature of such communication opportunities and the complicated effects of wireless interference in IEEE 802.11 networks. To address the challenge, we develop a model-driven optimization framework to jointly optimize opportunistic routes and rate limits for both unicast and multicast traffic. A distinctive feature of our framework is that the performance derived from optimization can be achieved in a real IEEE 802.11 network. Our framework consists of three key components: 1) a model for capturing the interference among IEEE 802.11 broadcast transmissions;2) a novel algorithm for accurately optimizing different performance objectives;and 3) effective techniques for mapping the resulting solutions to practical routing configurations. Extensive simulations and testbed experiments show that our approach significantly outperforms state-of-the-art shortest-path routing and opportunistic routing protocols. Moreover, the difference between the achieved performance and our model estimation is typically within 20%. Evaluation in dynamic and uncontrolled environments further shows that our approach is robust against inaccuracy introduced by a dynamic network and it also consistently outperforms the existing schemes. These results clearly demonstrate the effectiveness and accuracy of our approach.

关键词： model-driven optimization opportunistic routing WiFi model wireless mesh networks

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From single-objective to multi-objective reinforcement learning-based model transformation

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SOFTWARE AND SYSTEMS modelING 2024年 1-31页

作者： Eisenberg, Martin Wimmer, Manuel Johannes Kepler Univ Linz Inst Business Informat Software Engn Linz Austria

model-driven optimization allows to directly apply domain-specific modeling languages to define models which are subsequently optimized by applying a predefined set of model transformation rules. Objectives guide the optimization processes which can range from one single objective formulation resulting in one single solution to a set of objectives that necessitates the identification of a Pareto-optimal set of solutions. In recent years, a multitude of reinforcement learning approaches has been proposed that support both optimization cases and competitive results for various problem instances have been reported. However, their application to the field of model-driven optimization has not gained much attention yet, especially when compared to the extensive application of meta-heuristic search approaches such as genetic algorithms. Thus, there is a lack of knowledge about the applicability and performance of reinforcement learning for model-driven optimization. We therefore present in this paper a general framework for applying reinforcement learning to model-driven optimization problems. In particular, we show how a catalog of different reinforcement learning algorithms can be integrated with existing model-driven optimization approaches that use a transformation rule application encoding. We exemplify this integration by presenting a dedicated reinforcement learning extension for MOMoT. We build on this tool support and investigate several case studies for validating the applicability of reinforcement learning for model-driven optimization and compare the performance against a genetic algorithm. The results show clear advantages of using RL for single-objective problems, especially for cases where the transformation steps are highly dependent on each other. For multi-objective problems, the results are more diverse and case-specific, which further motivates the usage of model-driven optimization to utilize different approaches to find the best solutions.

关键词： model-driven optimization model transformation Reinforcement learning MOMoT

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Multi-objective model transformation chain exploration with MOMoT

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INFORMATION AND SOFTWARE TECHNOLOGY 2024年 174卷

作者： Eisenberg, Martin Sahay, Apurvanand Di Ruscio, Davide Iovino, Ludovico Wimmer, Manuel Pierantonio, Alfonso Johannes Kepler Univ Linz CDL MINT Linz Austria Amrita Vishwa Vidyapeetham Amrita Sch Comp Bengaluru India Univ Aquila Laquila Italy Gran Sasso Sci Inst Laquila Italy

Context: The increasing complexity of modern systems leads to an increasing amount of artifacts that are used along the model-based software and systems development lifecycle. This also includes model transformations, which serve for mapping models between representations, e.g., for verification and validation purposes. Objectives: model repositories manage this variety of artifacts and promote reusability, but should also enable the bundling of compatible artifacts. Therefore, model transformations should be reused and arranged into transformation chains to support more complex transformation scenarios. The resulting transformation should correspond to the user's interest in terms of quality criteria such as model coverage, transformation coverage, and number of transformation steps, thus assembling such chains becomes a multi -objective problem. Methods: A novel multi -objective approach for exploring possible transformation chains residing in model repositories is presented. MOMoT, a model-driven optimization framework, is leveraged to explore the transformation space spanned by the repository. For demonstration, three differently populated repositories are considered. Results: We have extended MOMoT with an exhaustive, multi -objective search that explores the entire model transformation space defined by graph transformation rules, allowing all possible transformation chains to be considered as solution. Accordingly, the optimal solutions were identified in the demonstration cases with negligible computation time. Conclusion: The approach assists modelers when there are multiple chains for transforming an input model to a specified output model to consider. Our evaluation shows that the approach elicits all legitimate transformation chains, thus enabling the modelers to consider trade-offs in view of multiple criteria selection.

关键词： model-driven optimization Transformation chain Multi-objective graph traversal model repository

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A generic construction for crossovers of graph-like structures and its realization in the Eclipse modeling Framework

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JOURNAL OF LOGICAL AND ALGEBRAIC METHODS IN PROGRAMMING 2024年 136卷

作者： Kosiol, Jens John, Stefan Taentzer, Gabriele Philipps Univ Marburg Marburg Germany Univ Kassel Kassel Germany

In model-driven optimization (MDO), domain-specific models are used to define and solve optimization problems via meta-heuristic search, often via evolutionary algorithms. models are typically evolved using mutations, which can be formally specified as graph transformations. So far, only mutations have been used to generate new solution models from existing ones;a crossover mechanism has not yet been developed. In this paper, we present a generic crossover construction for graph-like structures, which can be used to implement crossover operators for models in MDO. We prove basic properties of our construction and show how it can be used to implement a whole set of crossover operators that have been proposed for specific problems and situations on graphs. In particular, we present a specialization of our construction to models defined in the Eclipse modeling Framework.(c) 2023 Elsevier Inc. All rights reserved.

关键词： Evolutionary computation Crossover model-driven optimization Graph Category theory Eclipse modeling framework

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A Generic Construction for Crossovers of Graph-Like Structures 15th

A Generic Construction for Crossovers of Graph-Like Structur...

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15th International Conference on Graph Transformation (ICGT) Held as Part of Conference on Software Technologies - Applications and Foundations (STAF)

作者： Taentzer, Gabriele John, Stefan Kosiol, Jens Philipps Univ Marburg Marburg Germany

ISBN: (纸本)9783031098437;9783031098420

In model-driven optimization (MDO), domain-specific models are used to define and solve optimization problems with evolutionary algorithms. models are typically evolved using mutations, which can be formally specified as graph transformations. So far, only mutations have been used in MDO to generate new solutions from existing ones;a crossover mechanism has not yet been elaborated. In this paper, we present a generic crossover construction for graph-like structures that can be used to implement crossover operators in MDO. We prove basic properties of our construction and show how it can be used to implement a whole set of crossover operators that have been proposed for specific problems and situations on graphs.

关键词： Evolutionary Computation Crossover model-driven optimization Category Theory

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A Multiplicity-Preserving Crossover Operator on Graphs 22

A Multiplicity-Preserving Crossover Operator on Graphs

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ACM/IEEE 25th International Conference on model driven Engineering Languages and Systems (modelS)

作者： Thoelke, Henri Kosiol, Jens Philipps Univ Marburg Marburg Germany

ISBN: (纸本)9781450394673

Evolutionary algorithms usually explore a search space of solutions by means of crossover and mutation. While a mutation consists of a small, local modification of a solution, crossover mixes the genetic information of two solutions to compute a new one. For model-driven optimization (MDO), where models directly serve as possible solutions (instead of first transforming them into another representation), only recently a generic crossover operator has been developed. Using graphs as a formal foundation for models, we further refine this operator in such a way that additional well-formedness constraints are preserved: We prove that, given two models that satisfy a given set of multiplicity constraints as input, our refined crossover operator computes two new models as output that also satisfy the set of constraints.

关键词： evolutionary algorithms crossover model-driven optimization consistency-preservation

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modeling and optimization of High Frame Rate Video Transmission Over Wireless Networks

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2016年第4期15卷 2713-2726页

作者： Wu, Jiyan Yuen, Chau Cheung, Ngai-Man Chen, Junliang Chen, Chang Wen Singapore Univ Technol & Design Engn Prod Dev Pillar Singapore 487372 Singapore Singapore Univ Technol & Design Informat Syst Technol & Design Pillar Singapore 487372 Singapore Beijing Univ Posts & Telecommun Res Inst Networking & Switching Technol State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14228 USA

High frame rate (HFR) video is emerging as a new paradigm in popular multimedia applications (e.g., cloud gaming) to achieve smooth viewing experience perceived by end-users. In the context of HFR streaming video, end-to-end distortion and sending frame rate are equally important to the perceptual quality. This study presents a modeling-based approach to optimizing the HFR video transmission over wireless networks. First, we develop an analytical model dubbed FRIED (Frame Rate versus vIdEo Distortion) to characterize the tradeoff between sending frame rate and end-to-end video distortion. Second, we propose a Joint frAme Selection and FEC (Forward Error Correction) cOding (JASCO) approach based on the FRIED model to optimize the transmission performance. The efficacy of the proposed JASCO is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that JASCO outperforms the reference approaches in improving video peak signal-to-noise ratio (PSNR) at the same frame rate. Or conversely, JASCO is able to achieve higher received frame rate while guaranteeing the same video PSNR.

关键词： High frame rate video wireless video communication model-driven optimization forward error correction

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Automatic Tuning of Discrete Fourier Transforms driven by Analytical modeling 09

Automatic Tuning of Discrete Fourier Transforms Driven by An...

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18th International Conference on Parallel Architectures and Compilation Techniques

作者： Fraguela, Basilio B. Voronenko, Yevgen Pueschel, Markus Univ A Coruna Depto Elect & Sistemas La Coruna Spain Carnegie Mellon Univ Dept Elect & Comp Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9780769537719

Analytical models have been used to estimate optimal values for parameters such as the sizes in the context of loop nests. However, important algorithms such as fast Fourier transforms (FFTs) present a far more complex search space consisting of many thousands of different implementations with very different complex access patterns and nesting and code structures. As a results, some of the best available FFT implementations use heuristic search based on runtime measurements. In this paper we present the first analytical model that can successfully replace the measurement in this search on modern platforms. The model includes many details of the platform's memory system including the TLBs, and, for the first time, physically addressed caches and hardware prefetching. The effect, as we show, is a dramatically reduced search time to find the best FFT without significant loss in performance. Even though our model is adapted to the FFT in this paper, its underlying structure should be applicable for a much larger set of code structures and hence is a candidate for iterative compilation.

关键词： automatic performance tuning discrete Fourier transform FFT high-performance computing library generators model-driven optimization program optimization

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