17,438 research outputs found
Asynchronous Distributed Averaging on Communication Networks
Distributed algorithms for averaging have attracted interest in the control and sensing literature. However, previous works have not addressed some practical concerns that will arise in actual implementations on packet-switched communication networks such as the Internet. In this paper, we present several implementable algorithms that are robust to asynchronism and dynamic topology changes. The algorithms are completely distributed and do not require any global coordination. In addition, they can be proven to converge under very general asynchronous timing assumptions. Our results are verified by both simulation and experiments on Planetlab, a real-world TCP/IP network. We also present some extensions that are likely to be useful in applications
Deeply Learning the Messages in Message Passing Inference
Deep structured output learning shows great promise in tasks like semantic
image segmentation. We proffer a new, efficient deep structured model learning
scheme, in which we show how deep Convolutional Neural Networks (CNNs) can be
used to estimate the messages in message passing inference for structured
prediction with Conditional Random Fields (CRFs). With such CNN message
estimators, we obviate the need to learn or evaluate potential functions for
message calculation. This confers significant efficiency for learning, since
otherwise when performing structured learning for a CRF with CNN potentials it
is necessary to undertake expensive inference for every stochastic gradient
iteration. The network output dimension for message estimation is the same as
the number of classes, in contrast to the network output for general CNN
potential functions in CRFs, which is exponential in the order of the
potentials. Hence CNN message learning has fewer network parameters and is more
scalable for cases that a large number of classes are involved. We apply our
method to semantic image segmentation on the PASCAL VOC 2012 dataset. We
achieve an intersection-over-union score of 73.4 on its test set, which is the
best reported result for methods using the VOC training images alone. This
impressive performance demonstrates the effectiveness and usefulness of our CNN
message learning method.Comment: 11 pages. Appearing in Proc. The Twenty-ninth Annual Conference on
Neural Information Processing Systems (NIPS), 2015, Montreal, Canad
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Computing infrastructure issues in distributed communications systems : a survey of operating system transport system architectures
The performance of distributed applications (such as file transfer, remote login, tele-conferencing, full-motion video, and scientific visualization) is influenced by several factors that interact in complex ways. In particular, application performance is significantly affected both by communication infrastructure factors and computing infrastructure factors. Several communication infrastructure factors include channel speed, bit-error rate, and congestion at intermediate switching nodes. Computing infrastructure factors include (among other things) both protocol processing activities (such as connection management, flow control, error detection, and retransmission) and general operating system factors (such as memory latency, CPU speed, interrupt and context switching overhead, process architecture, and message buffering). Due to a several orders of magnitude increase in network channel speed and an increase in application diversity, performance bottlenecks are shifting from the network factors to the transport system factors.This paper defines an abstraction called an "Operating System Transport System Architecture" (OSTSA) that is used to classify the major components and services in the computing infrastructure. End-to-end network protocols such as TCP, TP4, VMTP, XTP, and Delta-t typically run on general-purpose computers, where they utilize various operating system resources such as processors, virtual memory, and network controllers. The OSTSA provides services that integrate these resources to support distributed applications running on local and wide area networks.A taxonomy is presented to evaluate OSTSAs in terms of their support for protocol processing activities. We use this taxonomy to compare and contrast five general-purpose commercial and experimental operating systems including System V UNIX, BSD UNIX, the x-kernel, Choices, and Xinu
A Calculus for Orchestration of Web Services
Service-oriented computing, an emerging paradigm for distributed computing based on the use of services, is calling for the development of tools and techniques to build safe and trustworthy systems, and to analyse their behaviour. Therefore, many researchers have proposed to use process calculi, a cornerstone of current foundational research on specification and analysis of concurrent, reactive, and distributed systems. In this paper, we follow this approach and introduce CWS, a process calculus expressly designed for specifying and combining service-oriented applications, while modelling their dynamic behaviour. We show that CWS can model all the phases of the life cycle of service-oriented applications, such as publication, discovery, negotiation, orchestration, deployment, reconfiguration and execution. We illustrate the specification style that CWS supports by means of a large case study from the automotive domain and a number of more specific examples drawn from it
Asynchronous Graph Pattern Matching on Multiprocessor Systems
Pattern matching on large graphs is the foundation for a variety of
application domains. Strict latency requirements and continuously increasing
graph sizes demand the usage of highly parallel in-memory graph processing
engines that need to consider non-uniform memory access (NUMA) and concurrency
issues to scale up on modern multiprocessor systems. To tackle these aspects,
graph partitioning becomes increasingly important. Hence, we present a
technique to process graph pattern matching on NUMA systems in this paper. As a
scalable pattern matching processing infrastructure, we leverage a
data-oriented architecture that preserves data locality and minimizes
concurrency-related bottlenecks on NUMA systems. We show in detail, how graph
pattern matching can be asynchronously processed on a multiprocessor system.Comment: 14 Pages, Extended version for ADBIS 201
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