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检索条件"任意字段=13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications"

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Energy-Efficient Scheduling of throughput-Constrained Streaming applications by Periodic Mode Switching 17

Energy-Efficient Scheduling of Throughput-Constrained Stream...

引用

17th Annual international conference on embedded Computer systems - Architectures, Modeling, and Simulation (SAMOS)

作者： Niknam, Sobhan Stefanov, Todor Leiden Univ Leiden Inst Adv Comp Sci Leiden Netherlands

ISBN: (纸本)9781538634370

In this paper, we address the problem of energy reduction when scheduling streaming applications with throughput constraints on homogeneous multiprocessor systems with voltage and frequency scaling capability. We propose a novel periodic scheduling framework which allows streaming applications to switch their execution periodically between a few energy-efficient schedules at run-time in order to meet a throughput constraint at long run. Using such periodic switching, we can benefit from adopting Dynamic Voltage and Frequency Scaling (DVFS) techniques to exploit available static slack time in the schedule of an application efficiently. the experimental results, on a set of real-life streaming applications, show that our novel scheduling approach can achieve up to 68% energy reduction depending on the application and the throughput constraint compared to related approaches.

关键词： energy conservation multiprocessing systems power aware computing processor scheduling scheduling power aware computing Energy conservation Multiprocessing systems scheduling of multiprocessor Energy efficiency Streaming throughput homogeneous multiprocessor

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An Approach for Hybrid Indoor/Outdoor Navigation 13

An Approach for Hybrid Indoor/Outdoor Navigation

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13th ieee international conference on Intelligent Environments (IE)

作者： Wagner, Stephan Fet, Ngewi Handte, Marcus Marron, Pedro Jose Univ Duisburg Essen Networked Embedded Syst Duisburg Germany

ISBN: (纸本)9781538619209

the wide availability of GPS-enabled devices has fueled the development of location-based applications. Among the most popular are navigation applications that guide users along their way. Due to the limited availability and precision of GPS in typical indoor environments, most navigation applications are limited to outdoor scenarios. To mitigate this, researchers and practitioners have been developing alternative systems to capture the position of a mobile device indoors. Using these systems, they clearly show that it is possible to provide precise location information at a reasonable cost. However, past applications in this domain have often focused exclusively on indoor scenarios. In this paper, we describe an approach to integrate the indoor and outdoor world to provide a seamless hybrid navigation experience. the approach builds upon our past work on indoor localization and integrates with our existing infrastructure for outdoor navigation that has been developed in the GAMBAS European research project. To evaluate the approach, we present data gathered from a real-world deployment as part of the SIMON European research project.

关键词： Global positioning system

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Revenue-Sensitive Scheduling of Multi-Application Tasks in Software-Defined Cloud 13

Revenue-Sensitive Scheduling of Multi-Application Tasks in S...

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13th ieee conference on Automation Science and Engineering (ieee CASE)

作者： Yuan, Haitao Bi, Jing Zhou, MengChu Beijing Jiaotong Univ Sch Software Engn Beijing 100044 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

ISBN: (纸本)9781509067817

the development of cloud computing attracts a growing number of corporations to implement their applications in data centers. the increase in variety and amount of applications in data centers that support software-defined networking (SDN) protocols makes it a big challenge to maximize revenue for data center providers. However, current SDN controllers just consider latency optimization in network and do not consider latency in virtual machines (VMs), and therefore revenue loss may occur. Different from current studies, this work aims to maximize revenue of a software-defined cloud provider. A Revenue-sensitive Scheduling of Multi-application Tasks (RSMT) method is then proposed to increase the revenue of a cloud provider. It is realized by jointly determining optimal routing paths and VMs for multi-application tasks. Simulation based on real-life task data demonstrates that compared with several current algorithms, RSMT can produce the efficient schedules that increase the cloud provider's revenue and decrease round trip time of multi-application tasks.

关键词： Routing Cloud computing Load management Resource management Control systems

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A Parallel Randomized Neural Network on In-memory Cluster computing for Big Data 13

A Parallel Randomized Neural Network on In-memory Cluster Co...

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13th international conference on Natural Computation, Fuzzy systems and Knowledge Discovery (ICNC-FSKD)

作者： Dai, Tongwu Li, Kenli Chen, Cen Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China Natl Supercomp Ctr Changsha Changsha 410082 Hunan Peoples R China

ISBN: (纸本)9781538621653

Owing to its good generalization performance and rapid learning speed, RNN (Randomized Neural Network) is widely used in text classification, image recognition, bioinformatics and other fields. However, the amount of data generated by applications in the real world is growing, and the traditional centralized randomized neural network (RNN) is no longer able to quickly and efficiently learn data that is so large. therefore, this paper makes use of the distributed framework provided by Flink to realize the randomized neural network (RNN) parallelization algorithm to make up for the deficiency of traditional centralized randomized neural network (RNN) algorithm in dealing with large datasets. By analyzing the characteristics of the traditional centralized randomized neural network (RNN), we find that the most time consuming part is the matrix multiplication operation of the Moore generalized inverse matrix when calculating the output weight vector. Since the matrix multiplication can be decomposed, the distributed matrix can be multiplied by the distributed framework provided by Flink, and then the corresponding output weight vector is centrally calculated. Extensive experiments have validated the effectiveness of our proposed algorithm.

关键词： Big data RNN Flink Parallel

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Digital Beethoven - An Android Based Virtual Piano 13

Digital Beethoven - An Android Based Virtual Piano

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13th international conference on Emerging Technologies (ICET)

作者： Begum, Hira Shaheen, Saima Moetesum, Momina Siddiqi, Imran Bahria Univ Dept Comp Sci Islamabad Pakistan

ISBN: (纸本)9781538622605

Smart phones with high resolution cameras have enabled innovative computer vision applications that apply techniques like real-time image processing and virtual reality to provide a close approximation of reality. In this paper, we present an Android application that allows users to play a virtual piano by using a keyboard drawn on a piece of paper. the application allows the user to point the camera of a hand held device towards the keyboard, and process image of the paper keyboard in real time. the application then detects fingers placed on a key and after key detection plays the corresponding sounds. Initial results demonstrate the potential of such an application in providing a viable replacement for heavy and expensive instruments.

关键词： Keyboards Cameras real-time systems Smart phones Image color analysis Skin Hardware

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Software systems for Processing and Analysis of Big Data and Event Streams 13

Software Systems for Processing and Analysis of Big Data and...

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13th international conference on Advanced Technologies, systems and Services in Telecommunications (TELSIKS)

作者： Stojnev, Aleksandra I. Stojanovic, Dragan H. Univ Nis Fac Elect Engn Aleksandra Medvedeva 14 Nish 18000 Serbia

ISBN: (纸本)9781538618004

Advances in sensor technologies, mobile positioning, IoT and wireless communications have led to development of applications for data collection, processing, analysis, mining and visualization for the purposes of real time responses and decision support, but have also brought new challenges to processing large amount of data in a real time. Various software systems are developed to provide a set of methods, technologies, algorithms and index structures for effective monitoring, processing, analysis and mining of continuous flow of data and events. this paper reviews recent developments in the field of data streams processing and analysis, gives an overview of existing systems and technologies, and demonstrates development of a stream processing application based on the contemporary stream processing frameworks.

关键词： Big data Stream processing and analysis Storm Spark Flink

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Collaborative Reuse of Streaming Dataflows in IoT applications 13

Collaborative Reuse of Streaming Dataflows in IoT Applicatio...

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13th Annual ieee international conference on e-Science (e-Science)

作者： Chaturvedi, Shilpa Tyagi, Sahil Simmhan, Yogesh Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781538626863

Distributed Stream Processing systems (DSPS) like Apache Storm and Spark Streaming enable composition of continuous dataflows that execute persistently over data streams. they are used by Internet of things (IoT) applications to analyze sensor data from Smart City cyber-infrastructure, and make active utility management decisions. As the ecosystem of such IoT applications that leverage shared urban sensor streams continue to grow, applications will perform duplicate pre-processing and analytics tasks. this offers the opportunity to collaboratively reuse the outputs of overlapping dataflows, thereby improving the resource efficiency. In this paper, we propose dataflow reuse algorithms that given a submitted dataflow, identifies the intersection of reusable tasks and streams from a collection of running dataflows to form a merged dataflow. Similar algorithms to unmerge dataflows when they are removed are also proposed. We implement these algorithms for the popular Apache Storm DSPS, and validate their performance and resource savings for 35 synthetic dataflows based on public OPMW workflows with diverse arrival and departure distributions, and on 21 real IoT dataflows from RIoTBench. We see that our Reuse algorithms reduce the count of running tasks by 38 - 46% for the two workloads, and a reduction in cumulative CPU usage of 36 - 51%, that can result in real cost savings on Cloud resources.

关键词： Cloud computing Storms Smart cities Kalman filters Sensors Sparks

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In the Heat of Conflict: On the Synchronisation of Critical Sections 20

In the Heat of Conflict: On the Synchronisation of Critical ...

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20th ieee international Symposium on real-time Distributed computing (ISORC)

作者： Reif, Stefan Honig, Timo Schroder-Preikschat, Wolfgang Friedrich Alexander Univ Erlangen Nurnberg FAU Dept Comp Sci Distributed Syst & Operating Syst Erlangen Germany

ISBN: (纸本)9781538615744

Advances in semiconductor technology greatly extend the scope of special-purpose applications as multi-core processors find the way into embedded systems. the increasing number of processor cores makes it more important than ever to have real-time operating systems process parallel threads in the most efficient way. In doing so, they have to pursue multiple (often conflicting) goals: namely being predictable as to time and energy demand. In shared-memory multi-core systems, contention at critical sections makes it inevitable for the operating system to execute competing threads with highly efficient synchronisation methods. Related research has primarily focussed on timing aspects of synchronisation methods, while the energy efficiency of the latter is an unexplored field, yet. In this paper, we implement and evaluate five distinct synchronisation methods and analyse their run-time characteristics (i.e., time, energy) in-depth. We evaluate the overall demand at application level, and empirically prove that contention increases the energy demand significantly even when competing processes are temporarily suspended. Furthermore, the evaluation reveals that choosing the right synchronisation method can decrease the energy demand by more than a factor of 5. We come to the conclusion that it is mandatory to consider the effects of process synchronisation for energy analysis and energy-efficiency optimisations.

关键词： embedded systems Energy-aware systems Performance and energy analysis real-time kernels and OS Synchronization System software

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Use of virtual load curves for the training of neural networks for residential electricity consumption forecasting applications

Use of virtual load curves for the training of neural networ...

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ieee/IAS international conference on Industry applications (INDUSCON)

作者： Haroldo L. M. do Amaral André N. de Souza Danilo S. Gastaldello thiago X. da S. Palma Alexander da S. Maranho João P. Papa Universidade de Sao Paulo Sao Paulo SÃ£o Paulo BR Laboratory of Power Systems and Intelligent Techniques - LSISPOTI São Paulo State University - UNESP Bauru - SP Brazil Laboratory of Power Systems and Intelligent Techniques - LSISPOTI Sacred Heart University - USC Bauru - SP Brazil

Smart grids are becoming increasingly closer to consumers, especially residential consumers, bringing with them a wide range of possibilities. the level of information obtained on a smart grid will be much higher when compared to a traditional network and at this point, more informed consumers tend to consume more efficiently, bringing benefits to themselves and to the system. An interesting fact for control within a residence is forecasting consumption, allowing the consumer to know in advance how much to consume up to a certain period. Artificial neural networks are one of several methods used to forecast time series, however, require a high volume of historical data for the training of the network, given that these may not be accessible or even exist. At this point, the objective of this work is to evaluate the use of load curves obtained through computational tools for the pre-training of artificial neural networks used in the consumption forecast. A tool is used to create random load curves according to the region and socioeconomic characteristics. the load curves are transformed into cumulative consumption curves and used as training vectors of the artificial neural network. the results of the tests were very promising, they showed that the pretraining with the virtual data makes possible the forecast of the time series even in the absence of real data for the training, showing that the methodology developed has great potential of application in works related to the forecast consumption.

关键词： Training Smart grids Artificial neural networks Forecasting time series analysis Tools

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the 13th ieee conference on Industrial Electronics and applications (ICIEA 2018)

Proceedings of the 13th IEEE Conference on Industrial Electr...

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Proceedings of the 13th ieee conference on Industrial Electronics and applications, ICIEA 2018 2018年 iii-iv页

作者： Zhu, Xing Chen, Kuisheng

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