Data-Mining Synthesised Schedulers for Hard Real-Time Systems

The analysis of hard real-time systems, traditionally performed using RMA/PCP or simulation, is nowadays also studied as a scheduler synthesis problem, where one automatically constructs a scheduler which can guarantee avoidance of deadlock and deadline-miss system states. Even though this approach has the potential for a finer control of a hard real-time system, using fewer resources and easily adapting to further quality aspects (memory/energy consumption, jitter minimisation, etc.), synthesised schedulers are usually extremely large and difficult to understand. Their big size is a consequence of their inherent precision, since they attempt to describe exactly the frontier among the safe and unsafe system states. It nevertheless hinders their application in practise, since it is extremely difficult to validate them or to use them for better understanding the behaviour of the system. In this paper, we show how one can adapt data-mining techniques to decrease the size of a synthesised scheduler and force its inherent structure to appear, thus giving the system designer a wealth of additional information for understanding and optimising the scheduler and the underlying system. We present, in particular, how it can be used for obtaining hints for a good task distribution to different processing units, for optimising the scheduler itself (sometimes even removing it altogether in a safe manner) and obtaining both per-task and per-system views of the schedulability of the system

Graphical modelling language for spycifying concurrency based on CSP

Introduced in this (shortened) paper is a graphical modelling language for specifying concurrency in software designs. The language notations are derived from CSP and the resulting designs form CSP diagrams. The notations reflect both data-flow and control-flow aspects of concurrent software architectures. These designs can automatically be described by CSP algebraic expressions that can be used for formal analysis. The designer does not have to be aware of the underlying mathematics. The techniques and rules presented provide guidance to the development of concurrent software architectures. One can detect and reason about compositional conflicts (errors in design), potential deadlocks (errors at run-time), and priority inversion problems (performance burden) at a high level of abstraction. The CSP diagram collaborates with objectoriented modelling languages and structured methods

Recycling controllers

The problem of designing control schemes for teams of robots to satisfy complex high-level tasks is a challenging problem which becomes more difficult when adding constraints on relative locations of robots. This paper presents a method for automatically creating hybrid controllers that ensure a team of heterogeneous robots satisfy some user specified high-level task while guaranteeing collision avoidance and predicting and reducing deadlock. The generated hybrid controller composes atomic controllers based on information the robots gather during runtime; thus these atomic controllers can be reused in different scenarios for multiple tasks. As a demonstration of this general approach we examine a task in which a group of robots sort different items to be recycled

Deadlock avoidance with virtual channels

High Performance Computing is a rapidly evolving area of computer science which attends to solve complicated computational problems with the combination of computational nodes connected through high speed networks. This work concentrates on the networks problems that appear in such networks and specially focuses on the Deadlock problem that can decrease the efficiency of the communication or even destroy the balance and paralyze the network. Goal of this work is the Deadlock avoidance with the use of virtual channels, in the switches of the network where the problem appears. The deadlock avoidance assures that will not be loss of data inside network, having as result the increased latency of the served packets, due to the extra calculation that the switches have to make to apply the policy.La computación de alto rendimiento es una zona de rápida evolución de la informática que busca resolver complicados problemas de cálculo con la combinación de los nodos de cómputo conectados a través de redes de alta velocidad. Este trabajo se centra en los problemas de las redes que aparecen en este tipo de sistemas y especialmente se centra en el problema del "deadlock" que puede disminuir la eficacia de la comunicación con la paralización de la red. El objetivo de este trabajo es la evitación de deadlock con el uso de canales virtuales, en los conmutadores de la red donde aparece el problema. Evitar el deadlock asegura que no se producirá la pérdida de datos en red, teniendo como resultado el aumento de la latencia de los paquetes, debido al overhead extra de cálculo que los conmutadores tienen que hacer para aplicar la política.La computació d'alt rendiment és una àrea de ràpida evolució de la informàtica que pretén resoldre complicats problemes de càlcul amb la combinació de nodes de còmput connectats a través de xarxes d'alta velocitat. Aquest treball se centra en els problemes de les xarxes que apareixen en aquest tipus de sistemes i especialment se centra en el problema del "deadlock" que pot disminuir l'eficàcia de la comunicació amb la paralització de la xarxa. L'objectiu d'aquest treball és l'evitació de deadlock amb l'ús de canals virtuals, en els commutadors de la xarxa on apareix el problema. Evitar deadlock assegura que no es produirà la pèrdua de dades en xarxa, tenint com a resultat l'augment de la latència dels paquets, degut al overhead extra de càlcul que els commutadors han de fer per aplicar la política

Simulationsgestützte Lösung von Deadlocks bei fahrerlosen Transportsystemen mit Hilfe von Deep Reinforcement Learning

This paper discusses the use of deep reinforcement learning to resolve deadlocks in material flow systems with automated guided vehicles (AGVs). The paper proposes a strategy for dealing with deadlocks based on a single Agent reinforcement learning approach (SARL). The agent will find the optimal solution strategy in real time. The proposed approach is evaluated using a material flow simulation for a real use case in industry. The effectiveness in reducing the occurrence of deadlocks as well as the number of collisions in the system is demonstrated. This study highlights the potential of deep reinforcement learning for improving the performance and efficiency of material flow systems with AGVs