16,727 research outputs found
Energy-Performance Trade-offs in Mobile Data Transfers
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
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
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
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
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
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
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
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
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
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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