10,293 research outputs found

    AltURI: a thin middleware for simulated robot vision applications

    Get PDF
    Fast software performance is often the focus when developing real-time vision-based control applications for robot simulators. In this paper we have developed a thin, high performance middleware for USARSim and other simulators designed for real-time vision-based control applications. It includes a fast image server providing images in OpenCV, Matlab or web formats and a simple command/sensor processor. The interface has been tested in USARSim with an Unmanned Aerial Vehicle using two control applications; landing using a reinforcement learning algorithm and altitude control using elementary motion detection. The middleware has been found to be fast enough to control the flying robot as well as very easy to set up and use

    Mesmerizer: A Effective Tool for a Complete Peer-to-Peer Software Development Life-cycle

    Get PDF
    In this paper we present what are, in our experience, the best practices in Peer-To-Peer(P2P) application development and how we combined them in a middleware platform called Mesmerizer. We explain how simulation is an integral part of the development process and not just an assessment tool. We then present our component-based event-driven framework for P2P application development, which can be used to execute multiple instances of the same application in a strictly controlled manner over an emulated network layer for simulation/testing, or a single application in a concurrent environment for deployment purpose. We highlight modeling aspects that are of critical importance for designing and testing P2P applications, e.g. the emulation of Network Address Translation and bandwidth dynamics. We show how our simulator scales when emulating low-level bandwidth characteristics of thousands of concurrent peers while preserving a good degree of accuracy compared to a packet-level simulator

    On properties of modeling control software for embedded control applications with CSP/CT framework

    Get PDF
    This PROGRESS project (TES.5224) traces a design framework for implementing embedded real-time software for control applications by exploiting its natural concurrency. The paper illustrates the stage of yielded automation in the process of structuring complex control software architectures, modeling controlled mechatronic systems and designing corresponding control laws, simulating them, generating control code out of simulated control strategy and implementing the software system on a (embedded) computer. The gap between the development of control strategies and the procedures of implementing them on chosen hardware targets is going to be overcome

    A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce

    Get PDF
    Nowadays many companies have available large amounts of raw, unstructured data. Among Big Data enabling technologies, a central place is held by the MapReduce framework and, in particular, by its open source implementation, Apache Hadoop. For cost effectiveness considerations, a common approach entails sharing server clusters among multiple users. The underlying infrastructure should provide every user with a fair share of computational resources, ensuring that Service Level Agreements (SLAs) are met and avoiding wastes. In this paper we consider two mathematical programming problems that model the optimal allocation of computational resources in a Hadoop 2.x cluster with the aim to develop new capacity allocation techniques that guarantee better performance in shared data centers. Our goal is to get a substantial reduction of power consumption while respecting the deadlines stated in the SLAs and avoiding penalties associated with job rejections. The core of this approach is a distributed algorithm for runtime capacity allocation, based on Game Theory models and techniques, that mimics the MapReduce dynamics by means of interacting players, namely the central Resource Manager and Class Managers

    OpenPING: A Reflective Middleware for the Construction of Adaptive Networked Game Applications

    Get PDF
    The emergence of distributed Virtual Reality (VR) applications that run over the Internet has presented networked game application designers with new challenges. In an environment where the public internet streams multimedia data and is constantly under pressure to deliver over widely heterogeneous user-platforms, there has been a growing need that distributed VR applications be aware of and adapt to frequent variations in their context of execution. In this paper, we argue that in contrast to research efforts targeted at improvement of scalability, persistence and responsiveness capabilities, much less attempts have been aimed at addressing the flexibility, maintainability and extensibility requirements in contemporary distributed VR platforms. We propose the use of structural reflection as an approach that not only addresses these requirements but also offers added value in the form of providing a framework for scalability, persistence and responsiveness that is itself flexible, maintainable and extensible. We also present an adaptive middleware platform implementation called OpenPING1 that supports our proposal in addressing these requirements

    Towards Exascale Scientific Metadata Management

    Full text link
    Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination between the data production and the analysis phases hinges on the availability of metadata that describe the scientific datasets. Existing workflow engines have been capturing a limited form of metadata to provide provenance information about the identity and lineage of the data. However, much of the data produced by simulations, experiments, and analyses still need to be annotated manually in an ad hoc manner by domain scientists. Systematic and transparent acquisition of rich metadata becomes a crucial prerequisite to sustain and accelerate the pace of scientific innovation. Yet, ubiquitous and domain-agnostic metadata management infrastructure that can meet the demands of extreme-scale science is notable by its absence. To address this gap in scientific data management research and practice, we present our vision for an integrated approach that (1) automatically captures and manipulates information-rich metadata while the data is being produced or analyzed and (2) stores metadata within each dataset to permeate metadata-oblivious processes and to query metadata through established and standardized data access interfaces. We motivate the need for the proposed integrated approach using applications from plasma physics, climate modeling and neuroscience, and then discuss research challenges and possible solutions
    • …
    corecore