16,727 research outputs found

    Energy-Performance Trade-offs in Mobile Data Transfers

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    By year 2020, the number of smartphone users globally will reach 3 Billion and the mobile data traffic (cellular + WiFi) will exceed PC internet traffic the first time. As the number of smartphone users and the amount of data transferred per smartphone grow exponentially, limited battery power is becoming an increasingly critical problem for mobile devices which increasingly depend on network I/O. Despite the growing body of research in power management techniques for the mobile devices at the hardware layer as well as the lower layers of the networking stack, there has been little work focusing on saving energy at the application layer for the mobile systems during network I/O. In this paper, to the best of our knowledge, we are first to provide an in depth analysis of the effects of application layer data transfer protocol parameters on the energy consumption of mobile phones. We show that significant energy savings can be achieved with application layer solutions at the mobile systems during data transfer with no or minimal performance penalty. In many cases, performance increase and energy savings can be achieved simultaneously

    GreenDataFlow: Minimizing the Energy Footprint of Global Data Movement

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    The global data movement over Internet has an estimated energy footprint of 100 terawatt hours per year, costing the world economy billions of dollars. The networking infrastructure together with source and destination nodes involved in the data transfer contribute to overall energy consumption. Although considerable amount of research has rendered power management techniques for the networking infrastructure, there has not been much prior work focusing on energy-aware data transfer solutions for minimizing the power consumed at the end-systems. In this paper, we introduce a novel application-layer solution based on historical analysis and real-time tuning called GreenDataFlow, which aims to achieve high data transfer throughput while keeping the energy consumption at the minimal levels. GreenDataFlow supports service level agreements (SLAs) which give the service providers and the consumers the ability to fine tune their goals and priorities in this optimization process. Our experimental results show that GreenDataFlow outperforms the closest competing state-of-the art solution in this area 50% for energy saving and 2.5x for the achieved end-to-end performance

    Energy-Efficient Mobile Network I/O Optimization at the Application Layer

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    Mobile data traffic (cellular + WiFi) will exceed PC Internet traffic by 2020. As the number of smartphone users and the amount of data transferred per smartphone grow exponentially, limited battery power is becoming an increasingly critical problem for mobile devices which depend on the network I/O. Despite the growing body of research in power management techniques for the mobile devices at the hardware layer as well as the lower layers of the networking stack, there has been little work focusing on saving energy at the application layer for the mobile systems during network I/O. In this paper, to the best of our knowledge, we are first to provide an in-depth analysis of the effects of application-layer data transfer protocol parameters on the energy consumption of mobile phones. We propose a novel model, called FastHLA, that can achieve significant energy savings at the application layer during mobile network I/O without sacrificing the performance. In many cases, our model achieves performance increase and energy saving simultaneously.Comment: arXiv admin note: text overlap with arXiv:1805.03970 and substantial text overlap with arXiv:1707.0682

    Energy-Efficient High-Throughput Data Transfers via Dynamic CPU Frequency and Core Scaling

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    The energy footprint of global data movement has surpassed 100 terawatt hours, costing more than 20 billion US dollars to the world economy. Depending on the number of switches, routers, and hubs between the source and destination nodes, the networking infrastructure consumes 10% - 75% of the total energy during active data transfers, and the rest is consumed by the end systems. Even though there has been extensive research on reducing the power consumption at the networking infrastructure, the work focusing on saving energy at the end systems has been limited to the tuning of a few application level parameters such as parallelism, pipelining, and concurrency. In this paper, we introduce three novel application-level parameter tuning algorithms which employ dynamic CPU frequency and core scaling, combining heuristics and runtime measurements to achieve energy efficient data transfers. Experimental results show that our proposed algorithms outperform the state-of-the-art solutions, achieving up to 48% reduced energy consumption and 80% higher throughput

    Application of Machine Learning in Wireless Networks: Key Techniques and Open Issues

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    As a key technique for enabling artificial intelligence, machine learning (ML) is capable of solving complex problems without explicit programming. Motivated by its successful applications to many practical tasks like image recognition, both industry and the research community have advocated the applications of ML in wireless communication. This paper comprehensively surveys the recent advances of the applications of ML in wireless communication, which are classified as: resource management in the MAC layer, networking and mobility management in the network layer, and localization in the application layer. The applications in resource management further include power control, spectrum management, backhaul management, cache management, beamformer design and computation resource management, while ML based networking focuses on the applications in clustering, base station switching control, user association and routing. Moreover, literatures in each aspect is organized according to the adopted ML techniques. In addition, several conditions for applying ML to wireless communication are identified to help readers decide whether to use ML and which kind of ML techniques to use, and traditional approaches are also summarized together with their performance comparison with ML based approaches, based on which the motivations of surveyed literatures to adopt ML are clarified. Given the extensiveness of the research area, challenges and unresolved issues are presented to facilitate future studies, where ML based network slicing, infrastructure update to support ML based paradigms, open data sets and platforms for researchers, theoretical guidance for ML implementation and so on are discussed.Comment: 34 pages,8 figure

    Recent Advances in Cloud Radio Access Networks: System Architectures, Key Techniques, and Open Issues

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    As a promising paradigm to reduce both capital and operating expenditures, the cloud radio access network (C-RAN) has been shown to provide high spectral efficiency and energy efficiency. Motivated by its significant theoretical performance gains and potential advantages, C-RANs have been advocated by both the industry and research community. This paper comprehensively surveys the recent advances of C-RANs, including system architectures, key techniques, and open issues. The system architectures with different functional splits and the corresponding characteristics are comprehensively summarized and discussed. The state-of-the-art key techniques in C-RANs are classified as: the fronthaul compression, large-scale collaborative processing, and channel estimation in the physical layer; and the radio resource allocation and optimization in the upper layer. Additionally, given the extensiveness of the research area, open issues and challenges are presented to spur future investigations, in which the involvement of edge cache, big data mining, social-aware device-to-device, cognitive radio, software defined network, and physical layer security for C-RANs are discussed, and the progress of testbed development and trial test are introduced as well.Comment: 27 pages, 11 figure

    Cloud Computing - Architecture and Applications

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    In the era of Internet of Things and with the explosive worldwide growth of electronic data volume, and associated need of processing, analysis, and storage of such humongous volume of data, it has now become mandatory to exploit the power of massively parallel architecture for fast computation. Cloud computing provides a cheap source of such computing framework for large volume of data for real-time applications. It is, therefore, not surprising to see that cloud computing has become a buzzword in the computing fraternity over the last decade. This book presents some critical applications in cloud frameworks along with some innovation design of algorithms and architecture for deployment in cloud environment. It is a valuable source of knowledge for researchers, engineers, practitioners, and graduate and doctoral students working in the field of cloud computing. It will also be useful for faculty members of graduate schools and universities.Comment: Edited Volume published by Intech Publishers, Croatia, June 2017. 138 pages. ISBN 978-953-51-3244-8, Print ISBN 978-953-51-3243-1. Link: https://www.intechopen.com/books/cloud-computing-architecture-and-application

    Machine Learning for Building Energy and Indoor Environment: A Perspective

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    Machine learning is a promising technique for many practical applications. In this perspective, we illustrate the development and application for machine learning. It is indicated that the theories and applications of machine learning method in the field of energy conservation and indoor environment are not mature, due to the difficulty of the determination for model structure with better prediction. In order to significantly contribute to the problems, we utilize the ANN model to predict the indoor culturable fungi concentration, which achieves the better accuracy and convenience. The proposal of hybrid method is further expand the application fields of machine learning method. Further, ANN model based on HTS was successfully applied for the optimization of building energy system. We hope that this novel method could capture more attention from investigators via our introduction and perspective, due to its potential development with accuracy and reliability. However, its feasibility in other fields needs to be promoted further.Comment: Submitted to a Interdisciplinary Journa

    A Multiobjective Optimization Framework for Routing in Wireless Ad Hoc Networks

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    Wireless ad hoc networks are seldom characterized by one single performance metric, yet the current literature lacks a flexible framework to assist in characterizing the design tradeoffs in such networks. In this work, we address this problem by proposing a new modeling framework for routing in ad hoc networks, which used in conjunction with metaheuristic multiobjective search algorithms, will result in a better understanding of network behavior and performance when multiple criteria are relevant. Our approach is to take a holistic view of the network that captures the cross-interactions among interference management techniques implemented at various layers of the protocol stack. The resulting framework is a complex multiobjective optimization problem that can be efficiently solved through existing multiobjective search techniques. In this contribution, we present the Pareto optimal sets for an example sensor network when delay, robustness and energy are considered. The aim of this paper is to present the framework and hence for conciseness purposes, the multiobjective optimization search is not developed herein

    Fundamentals of the Extremely Green, Flexible, and Profitable 5G M2M Ubiquitous Communications for Remote e-Healthcare and other Social e-Applications

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    The revolutionary trend of the up-to-date medicine can be formulated as wide introduction into basic medicine fields of electronic (e-health) and mobile (m-health) healthcare services and information applications. Unfortunately, all list of qualified m/e-healthcare services can be provided cost-effectively only in urban areas very good covered by broadband 4G/5G wireless communications. Unacceptably high investments are required into deployment of the optic core infrastructure for ubiquitous wide covering of sparsely populated rural, remote, and difficult for access (RRD) areas using the recent (4G) and forthcoming (5G) broadband radio access (RAN) centralized techniques, characterized by short cells ranges, because their profitability boundary exceeds several hundred residents per square km. Furthermore, the unprecedented requirements and new features of the forthcoming Internet of Things (IoT), machine-to-machine (M2M), and many other machine type IT-systems lead to a breakthrough in designing extremely green, flexible, and cost-effective technologies for future 5G wireless systems which will be able to reach in real time the performance extremums, trade-off optimums and fundamental limits. This paper examines the 5G PHY-MAC fundamentals and extremely approaches to creation of the profitable ubiquitous remote e/m-health services and telemedicine as the main innovation technology of popular healthcare and other social e-Applications for RRD territories. Proposed approaches lean on summarizing and develop the results of our previous works on RRD-adapted profitable ubiquitous green 4G/5G wireless multifunctional technologies.Comment: 6 pages, 8 figures, 2017 IEEE International Multi-Conference on Engineering, Computer and Information Sciences (SIBIRCON
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