26,739 research outputs found

    CUSTOMIZABLE FLASH FOR COMPUTING DEVICES

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    A computing device (e.g., a mobile phone, a camera, a tablet computer, etc.) may include a camera for capturing images (including a sequence of images that form videos). The camera may also include a flash or other light emitting element. As image processing has advanced and camera processors have become more powerful (in terms of processing cycles per given unit of time), the camera may capture images and perform image processing to generate processed images of relatively high quality even in low light conditions without necessarily requiring use of the flash. However, the flash may provide additional functionalities that may be unrelated to image capture (e.g., operating as a flashlight). Techniques described in this disclosure may provide an interface by which to configure the flash for use in providing the additional functionalities, allowing configuration of brightness, color temperature, strobe settings, etc

    Feature placement algorithms for high-variability applications in cloud environments

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    While the use of cloud computing is on the rise, many obstacles to its adoption remain. One of the weaknesses of current cloud offerings is the difficulty of developing highly customizable applications while retaining the increased scalability and lower cost offered by the multi-tenant nature of cloud applications. In this paper we describe a Software Product Line Engineering (SPLE) approach to the modelling and deployment of customizable Software as a Service (SaaS) applications. Afterwards we define a formal feature placement problem to manage these applications, and compare several heuristic approaches to solve the problem. The scalability and performance of the algorithms is investigated in detail. Our experiments show that the heuristics scale and perform well for systems with a reasonable load

    CLOUD COMPUTING BASED TECHNOLOGY INNOVATION

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    This paper attempts to define the concept of commodity computing. It is essentially about entrepreneurship based on the cloud computing technology. It is about how to build commercialized systems on the top of cloud computing technology (such as Azure) offered by public cloud computing vendors such as Microsoft. Current cloud computing technology has made it practical and financially attractive for an entrepreneurs to develop innovative IT services or products for a third party based on existing cloud computing offerings. A “Cloud Computing Commercialization Model” is proposed in this paper, with aims to build a bridge between the cloud computing technology and the technology-based entrepreneurship. More specifically, this paper explores how to develop customizable enterprise networks with existing technology available in a public cloud; these customizable enterprise networks have the commercial potentials to be delivered to any firm or organizations in many ways similar to a commodity like electricity

    VirtFogSim: A parallel toolbox for dynamic energy-delay performance testing and optimization of 5G Mobile-Fog-Cloud virtualized platforms

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    It is expected that the pervasive deployment of multi-tier 5G-supported Mobile-Fog-Cloudtechnological computing platforms will constitute an effective means to support the real-time execution of future Internet applications by resource- and energy-limited mobile devices. Increasing interest in this emerging networking-computing technology demands the optimization and performance evaluation of several parts of the underlying infrastructures. However, field trials are challenging due to their operational costs, and in every case, the obtained results could be difficult to repeat and customize. These emergingMobile-Fog-Cloud ecosystems still lack, indeed, customizable software tools for the performance simulation of their computing-networking building blocks. Motivated by these considerations, in this contribution, we present VirtFogSim. It is aMATLAB-supported software toolbox that allows the dynamic joint optimization and tracking of the energy and delay performance of Mobile-Fog-Cloud systems for the execution of applications described by general Directed Application Graphs (DAGs). In a nutshell, the main peculiar features of the proposed VirtFogSim toolbox are that: (i) it allows the joint dynamic energy-aware optimization of the placement of the application tasks and the allocation of the needed computing-networking resources under hard constraints on acceptable overall execution times, (ii) it allows the repeatable and customizable simulation of the resulting energy-delay performance of the overall system; (iii) it allows the dynamic tracking of the performed resource allocation under time-varying operational environments, as those typically featuring mobile applications; (iv) it is equipped with a user-friendly Graphic User Interface (GUI) that supports a number of graphic formats for data rendering, and (v) itsMATLAB code is optimized for running atop multi-core parallel execution platforms. To check both the actual optimization and scalability capabilities of the VirtFogSim toolbox, a number of experimental setups featuring different use cases and operational environments are simulated, and their performances are compared

    NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors

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    © 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation
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