2,717 research outputs found
Modeling, Simulation and Emulation of Intelligent Domotic Environments
Intelligent Domotic Environments are a promising approach, based on semantic models and commercially off-the-shelf domotic technologies, to realize new intelligent buildings, but such complexity requires innovative design methodologies and tools for ensuring correctness. Suitable simulation and emulation approaches and tools must be adopted to allow designers to experiment with their ideas and to incrementally verify designed policies in a scenario where the environment is partly emulated and partly composed of real devices. This paper describes a framework, which exploits UML2.0 state diagrams for automatic generation of device simulators from ontology-based descriptions of domotic environments. The DogSim simulator may simulate a complete building automation system in software, or may be integrated in the Dog Gateway, allowing partial simulation of virtual devices alongside with real devices. Experiments on a real home show that the approach is feasible and can easily address both simulation and emulation requirement
dReDBox: Materializing a full-stack rack-scale system prototype of a next-generation disaggregated datacenter
Current datacenters are based on server machines, whose mainboard and hardware components form the baseline, monolithic building block that the rest of the system software, middleware and application stack are built upon. This leads to the following limitations: (a) resource proportionality of a multi-tray system is bounded by the basic building block (mainboard), (b) resource allocation to processes or virtual machines (VMs) is bounded by the available resources within the boundary of the mainboard, leading to spare resource fragmentation and inefficiencies, and (c) upgrades must be applied to each and every server even when only a specific component needs to be upgraded. The dRedBox project (Disaggregated Recursive Datacentre-in-a-Box) addresses the above limitations, and proposes the next generation, low-power, across form-factor datacenters, departing from the paradigm of the mainboard-as-a-unit and enabling the creation of function-block-as-a-unit. Hardware-level disaggregation and software-defined wiring of resources is supported by a full-fledged Type-1 hypervisor that can execute commodity virtual machines, which communicate over a low-latency and high-throughput software-defined optical network. To evaluate its novel approach, dRedBox will demonstrate application execution in the domains of network functions virtualization, infrastructure analytics, and real-time video surveillance.This work has been supported in part by EU H2020 ICTproject dRedBox, contract #687632.Peer ReviewedPostprint (author's final draft
Maximising microprocessor reliability through game theory and heuristics
PhD ThesisEmbedded Systems are becoming ever more pervasive in our society, with most
routine daily tasks now involving their use in some form and the market predicted
to be worth USD 220 billion, a rise of 300%, by 2018. Consumers expect
more functionality with each design iteration, but for no detriment in perceived
performance. These devices can range from simple low-cost chips to expensive
and complex systems and are a major cost driver in the equipment design
phase. For more than 35 years, designers have kept pace with Moore's Law, but
as device size approaches the atomic limit, layouts are becoming so complicated
that current scheduling techniques are also reaching their limit, meaning that
more resource must be reserved to manage and deliver reliable operation. With
the advent of many-core systems and further sources of unpredictability such as
changeable power supplies and energy harvesting, this reservation of capability
may become so large that systems will not be operating at their peak efficiency.
These complex systems can be controlled through many techniques, with
jobs scheduled either online prior to execution beginning or online at each time
or event change. Increased processing power and job types means that current
online scheduling methods that employ exhaustive search techniques will not
be suitable to define schedules for such enigmatic task lists and that new techniques
using statistic-based methods must be investigated to preserve Quality
of Service.
A new paradigm of scheduling through complex heuristics is one way to
administer these next levels of processor effectively and allow the use of more
simple devices in complex systems; thus reducing unit cost while retaining reliability a key goal identified by the International Technology Roadmap for Semi-conductors for Embedded Systems in Critical Environments. These changes
would be beneficial in terms of cost reduction and system
exibility within the
next generation of device. This thesis investigates the use of heuristics and
statistical methods in the operation of real-time systems, with the feasibility of
Game Theory and Statistical Process Control for the successful supervision of
high-load and critical jobs investigated. Heuristics are identified as an effective
method of controlling complex real-time issues, with two-person non-cooperative
games delivering Nash-optimal solutions where these exist. The simplified algorithms for creating and solving Game Theory events allow for its use within
small embedded RISC devices and an increase in reliability for systems operating
at the apex of their limits. Within this Thesis, Heuristic and Game Theoretic
algorithms for a variety of real-time scenarios are postulated, investigated, refined and tested against existing schedule types; initially through MATLAB
simulation before testing on an ARM Cortex M3 architecture functioning as a
simplified automotive Electronic Control Unit.Doctoral Teaching Account from the EPSRC
AutoPlug: An Automotive Test-bed for Electronic Controller Unit Testing and Verification
In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. There is a need for new and scalable methods to evaluate automotive controls in a realistic and open setting. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) test-bed to diagnose, test, update and verify controls software. AutoPlug consists of multiple ECUs interconnected by a CAN bus, a race car driving simulator which behaves as the plant model and a vehicle controls monitor in Matlab. As the ECUs drive the simulated vehicle, the physicsbased simulation provides feedback to the controllers in terms of acceleration, yaw, friction and vehicle stability. This closedloop platform is then used to evaluate multiple vehicle control software modules such as traction, stability and cruise control. With this test-bed we highlight approaches for runtime ECU software diagnosis and testing of the stability and performance of the vehicle. Code updates can be executed via a smart phone so drivers may remotely “patch” their vehicle. This closedloop automotive control test-bed allows the automotive research community to explore the capabilities and challenges of safe and secure remote code updates for vehicle recalls management
Constraint-Aware, Scalable, and Efficient Algorithms for Multi-Chip Power Module Layout Optimization
Moving towards an electrified world requires ultra high-density power converters. Electric vehicles, electrified aerospace, data centers, etc. are just a few fields among wide application areas of power electronic systems, where high-density power converters are essential. As a critical part of these power converters, power semiconductor modules and their layout optimization has been identified as a crucial step in achieving the maximum performance and density for wide bandgap technologies (i.e., GaN and SiC). New packaging technologies are also introduced to produce reliable and efficient multichip power module (MCPM) designs to push the current limits. The complexity of the emerging MCPM layouts is surpassing the capability of a manual, iterative design process to produce an optimum design with agile development requirements. An electronic design automation tool called PowerSynth has been introduced with ongoing research toward enhanced capabilities to speed up the optimized MCPM layout design process. This dissertation presents the PowerSynth progression timeline with the methodology updates and corresponding critical results compared to v1.1. The first released version (v1.1) of PowerSynth demonstrated the benefits of layout abstraction, and reduced-order modeling techniques to perform rapid optimization of the MCPM module compared to the traditional, manual, and iterative design approach. However, that version is limited by several key factors: layout representation technique, layout generation algorithms, iterative design-rule-checking (DRC), optimization algorithm candidates, etc. To address these limitations, and enhance PowerSynth’s capabilities, constraint-aware, scalable, and efficient algorithms have been developed and implemented. PowerSynth layout engine has evolved from v1.3 to v2.0 throughout the last five years to incorporate the algorithm updates and generate all 2D/2.5D/3D Manhattan layout solutions. These fundamental changes in the layout generation methodology have also called for updates in the performance modeling techniques and enabled exploring different optimization algorithms. The latest PowerSynth 2 architecture has been implemented to enable electro-thermo-mechanical and reliability optimization on 2D/2.5D/3D MCPM layouts, and set up a path toward cabinet-level optimization. PowerSynth v2.0 computer-aided design (CAD) flow has been hardware-validated through manufacturing and testing of an optimized novel 3D MCPM layout. The flow has shown significant speedup compared to the manual design flow with a comparable optimization result
A component-based virtual engineering approach to PLC code generation for automation systems
In recent years, the automotive industry has been significantly affected by a number of challenges
driven by globalisation, economic fluctuations, environmental awareness and rapid technological developments.
As a consequence, product lifecycles are shortening and customer demands are becoming
more diverse. To survive in such a business environment, manufacturers are striving to find a costeffective
solution for fast and efficient development and reconfiguration of manufacturing systems to
satisfy the needs of changing markets without losses in production.
Production systems within automotive industry are vastly automated and heavily rely on PLC-based
control systems. It has been established that one of the major obstacles in realising reconfigurable
manufacturing systems is the fragmented engineering approach to implement control systems. Control
engineering starts at a very late stage in the overall system engineering process and remains highly
isolated from the mechanical design and build of the system. During this stage, control code is typically
written manually in vendor-specific tools in a combination of IEC 61131-3 languages. Writing
control code is a complex, time consuming and error-prone process. [Continues.
Gym-Ignition: Reproducible Robotic Simulations for Reinforcement Learning
This paper presents Gym-Ignition, a new framework to create reproducible
robotic environments for reinforcement learning research. It interfaces with
the new generation of Gazebo, part of the Ignition Robotics suite, which
provides three main improvements for reinforcement learning applications
compared to the alternatives: 1) the modular architecture enables using the
simulator as a C++ library, simplifying the interconnection with external
software; 2) multiple physics and rendering engines are supported as plugins,
simplifying their selection during the execution; 3) the new distributed
simulation capability allows simulating complex scenarios while sharing the
load on multiple workers and machines. The core of Gym-Ignition is a component
that contains the Ignition Gazebo simulator and exposes a simple interface for
its configuration and execution. We provide a Python package that allows
developers to create robotic environments simulated in Ignition Gazebo.
Environments expose the common OpenAI Gym interface, making them compatible
out-of-the-box with third-party frameworks containing reinforcement learning
algorithms. Simulations can be executed in both headless and GUI mode, the
physics engine can run in accelerated mode, and instances can be parallelized.
Furthermore, the Gym-Ignition software architecture provides abstraction of the
Robot and the Task, making environments agnostic on the specific runtime. This
abstraction allows their execution also in a real-time setting on actual
robotic platforms, even if driven by different middlewares.Comment: Accepted in SII202
Semantics and Execution of Domain Specific Models
In this paper we present a two-level approach to extend the abstract syntax of models with concrete semantics. First, a light-weight execution interface for iteratable models with a generic user interface allows the tool smith to provide arbitrary execution and visualization engine implementations for his or her Domain Specific Modeling Language (DSML). We discuss how the common execution manager runtime allows co-simulations of different model types and engine implementations to provide a flexible framework in the diverse DSML scenery. Second, as a concrete but nevertheless generic implementation of a simulation engine for behavior models, we present semantic model specifications and a runtime interfacing to the Ptolemy II tool suite. As a project in the area of model simulation, the latter provides a mature sophisticated and formally grounded backbone for model execution. We present our approach as an open source Eclipse integration to be an extension to the Eclipse modeling projects. After introducing basic concepts, the paper explains how simulations are currently being integrated into the framework and presents some illustrative case studies also covering UML approaches
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