48,760 research outputs found
A Survey of Energy Efficiency in SDN Software Based Methods and Optimization Models
Software Defined Networking (SDN) paradigm has the benefits of programmable
network elements by separating the control and the forwarding planes,
efficiency through optimized routing and flexibility in network management. As
the energy costs contribute largely to the overall costs in networks, energy
efficiency has become a significant design requirement for modern networking
mechanisms. However, designing energy efficient solutions is non-trivial since
they need to tackle the trade-off between energy efficiency and network
performance. In this article, we address the energy efficiency capabilities
that can be utilized in the emerging SDN. We provide a comprehensive and novel
classification of software-based energy efficient solutions into subcategories
of traffic aware, end system aware and rule placement. We propose general
optimization models for each subcategory, and present the objective function,
the parameters and constraints to be considered in each model. Detailed
information on the characteristics of state-of-the-art methods, their
advantages, drawbacks are provided. Hardware-based solutions used to enhance
the efficiency of switches are also described. Furthermore, we discuss the open
issues and future research directions in the area of energy efficiency in SDN.Comment: 17 double column pages, 3 figures, 6 table
Optimal distance- and time-dependent area-based pricing with the Network Fundamental Diagram
Given the efficiency and equity concerns of a cordon toll, this paper
proposes a few alternative distance-dependent area-based pricing models for a
large-scale dynamic traffic network. We use the Network Fundamental Diagram
(NFD) to monitor the network traffic state over time and consider different
trip lengths in the toll calculation. The first model is a distance toll that
is linearly related to the distance traveled within the cordon. The second
model is an improved joint distance and time toll (JDTT) whereby users are
charged jointly in proportion to the distance traveled and time spent within
the cordon. The third model is a further improved joint distance and delay toll
(JDDT) which replaces the time toll in the JDTT with a delay toll component. To
solve the optimal toll level problem, we develop a simulation-based
optimization (SBO) framework. Specifically, we propose a simultaneous approach
and a sequential approach, respectively, based on the proportional-integral
(PI) feedback controller to iteratively adjust the JDTT and JDDT, and use a
calibrated large-scale simulation-based dynamic traffic assignment (DTA) model
of Melbourne, Australia to evaluate the network performance under different
pricing scenarios. While the framework is developed for static pricing, we show
that it can be easily extended to solve time-dependent pricing by using
multiple PI controllers. Results show that although the distance toll keeps the
network from entering the congested regime of the NFD, it naturally drives
users into the shortest paths within the cordon resulting in an uneven
distribution of congestion. This is reflected by a large clockwise hysteresis
loop in the NFD. In contrast, both the JDTT and JDDT reduce the size of the
hysteresis loop while achieving the same control objective.Comment: 39 pages, 13 figure
Surrogate-based toll optimization in a large-scale heterogeneously congested network
Toll optimization in a large-scale dynamic traffic network is typically
characterized by an expensive-to-evaluate objective function. In this paper, we
propose two toll level problems (TLPs) integrated with a large-scale
simulation-based dynamic traffic assignment (DTA) model of Melbourne,
Australia. The first TLP aims to control the pricing zone (PZ) through a
time-varying joint distance and delay toll (JDDT) such that the network
fundamental diagram (NFD) of the PZ does not enter the congested regime. The
second TLP is built upon the first TLP by further considering the minimization
of the heterogeneity of congestion distribution in the PZ. To solve the two
TLPs, a computationally efficient surrogate-based optimization method, i.e.,
regressing kriging (RK) with expected improvement (EI) sampling, is applied to
approximate the simulation input-output mapping, which can balance well between
local exploitation and global exploration. Results show that the two optimal
TLP solutions reduce the average travel time in the PZ (entire network) by
29.5% (1.4%) and 21.6% (2.5%), respectively. Reducing the heterogeneity of
congestion distribution achieves higher network flows in the PZ and a lower
average travel time or a larger total travel time saving in the entire network.Comment: 16 pages, 7 figure
Robust Resource Allocation with Joint Carrier Aggregation for Multi-Carrier Cellular Networks
In this paper, we present a novel approach for robust optimal resource
allocation with joint carrier aggregation to allocate multiple carriers
resources optimally among users with elastic and inelastic traffic in cellular
networks. We use utility proportional fairness allocation policy, where the
fairness among users is in utility percentage of the application running on the
user equipment (UE). Each UE is assigned an application utility function based
on the type of its application. Our objective is to allocate multiple carriers
resources optimally among users subscribing for mobile services. In addition,
each user is guaranteed a minimum quality of service (QoS) that varies based on
the user's application type. We present a robust algorithm that solves the
drawback in the algorithm presented in [1] by preventing the fluctuations in
the resource allocation process, in the case of scarce resources, and allocates
optimal rates for both high-traffic and low-traffic situations. Our distributed
resource allocation algorithm allocates an optimal rate to each user from all
carriers in its range while providing the minimum price for the allocated rate.
In addition, we analyze the convergence of the algorithm with different network
traffic densities and show that our algorithm provides traffic dependent
pricing for network providers. Finally, we present simulation results for the
performance of our resource allocation algorithm.Comment: Submitted to IEEE. Part of this work has been uploaded to
arXiv:1405.644
Cross Layer Provision of Future Cellular Networks
To cope with the growing demand for wireless data and to extend service
coverage, future 5G networks will increasingly rely on the use of low powered
nodes to support massive connectivity in diverse set of applications and
services [1]. To this end, virtualized and mass-scale cloud architectures are
proposed as promising technologies for 5G in which all the nodes are connected
via a backhaul network and managed centrally by such cloud centers. The
significant computing power made available by the cloud technologies has
enabled the implementation of sophisticated signal processing algorithms,
especially by way of parallel processing, for both interference management and
network provision. The latter two are among the major signal processing tasks
for 5G due to increased level of frequency sharing, node density, interference
and network congestion. This article outlines several theoretical and practical
aspects of joint interference management and network provisioning for future 5G
networks. A cross-layer optimization framework is proposed for joint user
admission, user-base station association, power control, user grouping,
transceiver design as well as routing and flow control. We show that many of
these cross-layer tasks can be treated in a unified way and implemented in a
parallel manner using an efficient algorithmic framework called WMMSE (Weighted
MMSE). Some recent developments in this area are highlighted and future
research directions are identified
Management and Orchestration of Network Slices in 5G, Fog, Edge and Clouds
Network slicing allows network operators to build multiple isolated virtual
networks on a shared physical network to accommodate a wide variety of services
and applications. With network slicing, service providers can provide a
cost-efficient solution towards meeting diverse performance requirements of
deployed applications and services. Despite slicing benefits, End-to-End
orchestration and management of network slices is a challenging and complicated
task. In this chapter, we intend to survey all the relevant aspects of network
slicing, with the focus on networking technologies such as Software-defined
networking (SDN) and Network Function Virtualization (NFV) in 5G, Fog/Edge and
Cloud Computing platforms. To build the required background, this chapter
begins with a brief overview of 5G, Fog/Edge and Cloud computing, and their
interplay. Then we cover the 5G vision for network slicing and extend it to the
Fog and Cloud computing through surveying the state-of-the-art slicing
approaches in these platforms. We conclude the chapter by discussing future
directions, analyzing gaps and trends towards the network slicing realization.Comment: 31 pages, 4 figures, Fog and Edge Computing: Principles and
Paradigms, Wiley Press, New York, USA, 201
Gramian-Based Optimization for the Analysis and Control of Traffic Networks
This paper proposes a simplified version of classical models for urban
transportation networks, and studies the problem of controlling intersections
with the goal of optimizing network-wide congestion. Differently from
traditional approaches to control traffic signaling, a simplified framework
allows for a more tractable analysis of the network overall dynamics, and
enables the design of critical parameters while considering network-wide
measures of efficiency. Motivated by the increasing availability of real-time
high-resolution traffic data, we cast an optimization problem that formalizes
the goal of minimizing the overall network congestion by optimally controlling
the durations of green lights at intersections. Our formulation allows us to
relate congestion objectives with the problem of optimizing a metric of
controllability of an associated dynamical network. We then provide a technique
to efficiently solve the optimization by parallelizing the computation among a
group of distributed agents. Lastly, we assess the benefits of the proposed
modeling and optimization framework through microscopic simulations on typical
traffic commute scenarios for the area of Manhattan. The optimization framework
proposed in this study is made available online on a Sumo microscopic simulator
based interface [1]
Wireless Network Design for Control Systems: A Survey
Wireless networked control systems (WNCS) are composed of spatially
distributed sensors, actuators, and con- trollers communicating through
wireless networks instead of conventional point-to-point wired connections. Due
to their main benefits in the reduction of deployment and maintenance costs,
large flexibility and possible enhancement of safety, WNCS are becoming a
fundamental infrastructure technology for critical control systems in
automotive electrical systems, avionics control systems, building management
systems, and industrial automation systems. The main challenge in WNCS is to
jointly design the communication and control systems considering their tight
interaction to improve the control performance and the network lifetime. In
this survey, we make an exhaustive review of the literature on wireless network
design and optimization for WNCS. First, we discuss what we call the critical
interactive variables including sampling period, message delay, message
dropout, and network energy consumption. The mutual effects of these
communication and control variables motivate their joint tuning. We discuss the
effect of controllable wireless network parameters at all layers of the
communication protocols on the probability distribution of these interactive
variables. We also review the current wireless network standardization for WNCS
and their corresponding methodology for adapting the network parameters.
Moreover, we discuss the analysis and design of control systems taking into
account the effect of the interactive variables on the control system
performance. Finally, we present the state-of-the-art wireless network design
and optimization for WNCS, while highlighting the tradeoff between the
achievable performance and complexity of various approaches. We conclude the
survey by highlighting major research issues and identifying future research
directions.Comment: 37 pages, 17 figures, 4 table
SkyLiTE: End-to-End Design of Low-Altitude UAV Networks for Providing LTE Connectivity
Un-manned aerial vehicle (UAVs) have the potential to change the landscape of
wide-area wireless connectivity by bringing them to areas where connectivity
was sparing or non-existent (e.g. rural areas) or has been compromised due to
disasters. While Google's Project Loon and Facebook's Project Aquila are
examples of high-altitude, long-endurance UAV-based connectivity efforts in
this direction, the telecom operators (e.g. AT&T and Verizon) have been
exploring low-altitude UAV-based LTE solutions for on-demand deployments.
Understandably, these projects are in their early stages and face formidable
challenges in their realization and deployment. The goal of this document is to
expose the reader to both the challenges as well as the potential offered by
these unconventional connectivity solutions. We aim to explore the end-to-end
design of such UAV-based connectivity networks particularly in the context of
low-altitude UAV networks providing LTE connectivity. Specifically, we aim to
highlight the challenges that span across multiple layers (access, core
network, and backhaul) in an inter-twined manner as well as the richness and
complexity of the design space itself. To help interested readers navigate this
complex design space towards a solution, we also articulate the overview of one
such end-to-end design, namely SkyLiTE-- a self-organizing network of
low-altitude UAVs that provide optimized LTE connectivity in a desired region
Machine Learning for Vehicular Networks
The emerging vehicular networks are expected to make everyday vehicular
operation safer, greener, and more efficient, and pave the path to autonomous
driving in the advent of the fifth generation (5G) cellular system. Machine
learning, as a major branch of artificial intelligence, has been recently
applied to wireless networks to provide a data-driven approach to solve
traditionally challenging problems. In this article, we review recent advances
in applying machine learning in vehicular networks and attempt to bring more
attention to this emerging area. After a brief overview of the major concept of
machine learning, we present some application examples of machine learning in
solving problems arising in vehicular networks. We finally discuss and
highlight several open issues that warrant further research.Comment: Accepted by IEEE Vehicular Technology Magazin
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