20,486 research outputs found

    Programming and Deployment of Active Objects with Application-Level Scheduling

    Get PDF
    We extend and implement a modeling language based on concurrent active objects with application-level scheduling policies. The language allows a programmer to assign pri- orities at the application level, for example, to method def- initions and method invocations, and assign corresponding policies to the individual active objects for scheduling the messages. Thus, we leverage scheduling and performance related issues, which are becoming increasingly important in multi-core and cloud applications, from the underlying operating system to the application level. We describe a tool-set to transform models of active objects extended with application-level scheduling policies into Java. This tool-set allows a direct use of Java class libraries; thus, we obtain a full-fledged programming language based on active objects which allows for high-level control of deployment related is- sues

    Programming and deployment of active objects with application-level scheduling

    Full text link
    We extend and implement a modeling language based on concurrent active objects with application-level scheduling policies. The language allows a programmer to assign pri-orities at the application level, for example, to method def-initions and method invocations, and assign corresponding policies to the individual active objects for scheduling the messages. Thus, we leverage scheduling and performance related issues, which are becoming increasingly important in multi-core and cloud applications, from the underlying operating system to the application level. We describe a tool-set to transform models of active objects extended with application-level scheduling policies into Java. This tool-set allows a direct use of Java class libraries; thus, we obtain a full-fledged programming language based on active objects which allows for high-level control of deployment related is-sues

    Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

    Full text link
    Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

    Revisiting Actor Programming in C++

    Full text link
    The actor model of computation has gained significant popularity over the last decade. Its high level of abstraction makes it appealing for concurrent applications in parallel and distributed systems. However, designing a real-world actor framework that subsumes full scalability, strong reliability, and high resource efficiency requires many conceptual and algorithmic additives to the original model. In this paper, we report on designing and building CAF, the "C++ Actor Framework". CAF targets at providing a concurrent and distributed native environment for scaling up to very large, high-performance applications, and equally well down to small constrained systems. We present the key specifications and design concepts---in particular a message-transparent architecture, type-safe message interfaces, and pattern matching facilities---that make native actors a viable approach for many robust, elastic, and highly distributed developments. We demonstrate the feasibility of CAF in three scenarios: first for elastic, upscaling environments, second for including heterogeneous hardware like GPGPUs, and third for distributed runtime systems. Extensive performance evaluations indicate ideal runtime behaviour for up to 64 cores at very low memory footprint, or in the presence of GPUs. In these tests, CAF continuously outperforms the competing actor environments Erlang, Charm++, SalsaLite, Scala, ActorFoundry, and even the OpenMPI.Comment: 33 page

    Kompics: a message-passing component model for building distributed systems

    Get PDF
    The Kompics component model and programming framework was designedto simplify the development of increasingly complex distributed systems. Systems built with Kompics leverage multi-core machines out of the box and they can be dynamically reconfigured to support hot software upgrades. A simulation framework enables deterministic debugging and reproducible performance evaluation of unmodified Kompics distributed systems. We describe the component model and show how to program and compose event-based distributed systems. We present the architectural patterns and abstractions that Kompics facilitates and we highlight a case study of a complex distributed middleware that we have built with Kompics. We show how our approach enables systematic development and evaluation of large-scale and dynamic distributed systems

    High-Performance Cloud Computing: A View of Scientific Applications

    Full text link
    Scientific computing often requires the availability of a massive number of computers for performing large scale experiments. Traditionally, these needs have been addressed by using high-performance computing solutions and installed facilities such as clusters and super computers, which are difficult to setup, maintain, and operate. Cloud computing provides scientists with a completely new model of utilizing the computing infrastructure. Compute resources, storage resources, as well as applications, can be dynamically provisioned (and integrated within the existing infrastructure) on a pay per use basis. These resources can be released when they are no more needed. Such services are often offered within the context of a Service Level Agreement (SLA), which ensure the desired Quality of Service (QoS). Aneka, an enterprise Cloud computing solution, harnesses the power of compute resources by relying on private and public Clouds and delivers to users the desired QoS. Its flexible and service based infrastructure supports multiple programming paradigms that make Aneka address a variety of different scenarios: from finance applications to computational science. As examples of scientific computing in the Cloud, we present a preliminary case study on using Aneka for the classification of gene expression data and the execution of fMRI brain imaging workflow.Comment: 13 pages, 9 figures, conference pape
    • …
    corecore