Performance evaluation of discret event systems using P-time Event Graphs

The dater equalities constitutes an appropriate tool which allows a linear description of timed event graph in the field of (max,+) algebra. This paper give an equivalent model in the standard algebra. The application of a variant of Farkas\u27lemma allow the necessary condition of existence of upper and lower bounds of the cycle time. A linear programming defined on the particular incidence matrix of the P-time event graph are used to compute the cycle time

Local Difference Measures between Complex Networks for Dynamical System Model Evaluation

Acknowledgments We thank Reik V. Donner for inspiring suggestions that initialized the work presented herein. Jan H. Feldhoff is credited for providing us with the STARS simulation data and for his contributions to fruitful discussions. Comments by the anonymous reviewers are gratefully acknowledged as they led to substantial improvements of the manuscript.Peer reviewedPublisher PD

Simulation of Large Scale Computational Ecosystems with Alchemist: A Tutorial

Many interesting systems in several disciplines can be modeled as networks of nodes that can store and exchange data: pervasive systems, edge computing scenarios, and even biological and bio-inspired systems. These systems feature inherent complexity, and often simulation is the preferred (and sometimes the only) way of investigating their behavior; this is true both in the design phase and in the verification and testing phase. In this tutorial paper, we provide a guide to the simulation of such systems by leveraging Alchemist, an existing research tool used in several works in the literature. We introduce its meta-model and its extensible architecture; we discuss reference examples of increasing complexity; and we finally show how to configure the tool to automatically execute multiple repetitions of simulations with different controlled variables, achieving reliable and reproducible results

Limiting Properties of Random Graph Models with Vertex and Edge Weights

This paper provides an overview of results, concerning longest or heaviest paths, in the area of random directed graphs on the integers along with some extensions. We study first-order asymptotics of heaviest paths allowing weights both on edges and vertices and assuming that weights on edges are signed. We aim at an exposition that summarizes, simplifies, and extends proof ideas. We also study sparse graph asymptotics, showing convergence of the weighted random graphs to a certain weighted graph that can be constructed in terms of Poisson processes. We are motivated by numerous applications, ranging from ecology to parallel computing model. It is the latter set of applications that necessitates the introduction of vertex weights. Finally, we discuss some open problems and research directions

Efficient, decentralized detection of qualitative spatial events in a dynamic scalar field

This paper describes an efficient, decentralized algorithm to monitor qualitative spatial events in a dynamic scalar field. The events of interest involve changes to the critical points (i.e., peak, pits and passes) and edges of the surface network derived from the field. Four fundamental types of event (appearance, disappearance, movement and switch) are defined. Our algorithm is designed to rely purely on qualitative information about the neighborhoods of nodes in the sensor network and does not require information about nodes' coordinate positions. Experimental investigations confirm that our algorithm is efficient, with O(n) overall communication complexity (where n is the number of nodes in the sensor network), an even load balance and low operational latency. The accuracy of event detection is comparable to established centralized algorithms for the identification of critical points of a surface network. Our algorithm is relevant to a broad range of environmental monitoring applications of sensor networks

Autonomic Management of Reconfigurable Embedded Systems using Discrete Control: Application to FPGA

This paper targets the autonomic management of dynamically partially reconfigurable hardware architectures based on FPGAs. Such hardware-level autonomic computing has been less often studied than at software-level. We consider control techniques to model the considered behaviours of the computing system and derive a controller for the control objective enforcement. Discrete Control modelled with Labelled Transition Systems is employed in this paper. Such models are amenable to Discrete Controller Synthesis algorithms that can automatically generate a controller enforcing the correct behaviours of a controlled system. A general modelling framework is proposed for the control of FPGA based computing systems. We consider system application described as task graphs and FPGA as a set of reconfigurable areas that can be dynamically partially reconfigured to execute tasks. We encode the computation of an autonomic manager as a DCS problem w.r.t. multiple constraints and objectives e.g., mutual exclusion of resource uses, power cost minimization. We validate our models and manager computations by using the BZR language and an experimental demonstrator implemented on a Xilinx FPGA platform.Nous traitons de la gestion autonomique des architectures matérielles dynamique- ment et partiellement reconfigurables á base de FPGAs. Cette forme d'informatique autonomique au niveau matériel a été moins souvent étudié qu'au niveau logiciel. Nous considérons des tech- niques de contrôle pour modéliser les comportements du système de calcul et pour dériver un contrôleur pour le maintien de l'objectif de contrôle. Nous utilisons des techniques de contrôle discret modélisé avec des systèmes de transition étiquetés. Ces modèles se prêtent à une algorith- mique de synthèse de contrôleurs discrets (SCD) qui peut générer automatiquement un contrôleur qui force les comportements corrects d'un système contrôlé. Un cadre général de modélisation est proposé pour le contrôle des systèmes informatiques à base de FPGA. Nous considérons que l'application est décrite par un graphes de tâches, et le FPGA comme un ensemble de zones reconfigurables, qui peuvent être dynamiquement et partiellement reconfigurées pour exécuter des tâches. Nous formulons le calcul d'un gestionnaire autonomique comme un problème de SCD concernant des contraintes et objectifs multiples, par exemple, l'exclusion mutuelle de l'utilisation des ressources, la minimisation du coût en énergie. Nous validons nos modèles et les calculs du gestionnaire en utilisant le langage BZR et un démonstrateur expérimental mis en œuvre sur une plate-forme FPGA Xilinx

Time-optimal control of large-scale systems of systems using compositional optimization

Optimization of industrial processes such as manufacturing cells can have great impact on their performance. Finding optimal solutions to these large-scale systems is, however, a complex problem. They typically include multiple subsystems, and the search space generally grows exponentially with each subsystem. In previous work we proposed Compositional Optimization as a method to solve these type of problems. This integrates optimization with techniques from compositional supervisory control, dividing the optimization into separate sub-problems. The main purpose is to mitigate the state explosion problem, but a bonus is that the individual sub-problems can be solved using parallel computation, making the method even more scalable. This paper further improves on compositional optimization with a novel synchronization method, called partial time-weighted synchronization (PTWS), that is specifically designed for time-optimal control of asynchronous systems. The benefit is its ability to combine the behaviour of asynchronous subsystems without introducing additional states or transitions. The method also reduces the search space further by integrating an optimization heuristic that removes many non-optimal or redundant solutions already during synchronization. Results in this paper show that compositional optimization efficiently generates global optimal solutions to large-scale realistic optimization problems, too big to solve when based on traditional monolithic models. It is also shown that the introduction of PTWS drastically decreases the total search space of the optimization compared to previous work

Symbolic Synthesis of Neural Networks

Neural networks adapt very well to distributed and continuous representations, but struggle to generalize from small amounts of data. Symbolic systems commonly achieve data efficient generalization by exploiting modularity to benefit from local and discrete features of a representation. These features allow symbolic programs to be improved one module at a time and to experience combinatorial growth in the values they can successfully process. However, it is difficult to design a component that can be used to form symbolic abstractions and which is adequately overparametrized to learn arbitrary high-dimensional transformations. I present Graph-based Symbolically Synthesized Neural Networks (G-SSNNs), a class of neural modules that operate on representations modified with synthesized symbolic programs to include a fixed set of local and discrete features. I demonstrate that the choice of injected features within a G-SSNN module modulates the data efficiency and generalization of baseline neural models, creating predictable patterns of both heightened and curtailed generalization. By training G-SSNNs, we also derive information about desirable semantics of symbolic programs without manual engineering. This information is compact and amenable to abstraction, but can also be flexibly recontextualized for other high-dimensional settings. In future work, I will investigate data efficient generalization and the transferability of learned symbolic representations in more complex G-SSNN designs based on more complex classes of symbolic programs. Experimental code and data are available at https://github.com/shlomenu/symbolically_synthesized_networks .Comment: 8 pages, 1 figure. Minor formula correction and minor textual revisio